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
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	2789	25	2237	24	1613	25	2006	20	45	81	292	58	306	62	0	.	740	48	1226	35	1480	34	1682	38	1	100	3390	24	5266	19	3203	20	5078	19	9355	12	5785	15	1642	29	515	51	797	38	963	36	139	60	755	43	363	58	347	46	587	51	813	56	1429	32	1826	29	288	51	1099	38	1893	31	1236	43	1165	44	1082	34	904	43	294	44	271	54	290	60	1714	30	1527	30	948	38	166	73	295	58	348	58	1088	39	3303	38	1459	31	54	100	45	100	0	.	417	54	212	63	612	37	633	47	765	44	1492	34	973	53	1366	35	259	59	693	44	29	100	951	37	138	68	253	51	1441	26	665	41	769	40	586	46	128	80	212	52	32	75	242	98	3838	20	3576	21	5584	17	674	43	357	47	426	42	310	52	557	54	10	100	427	52	348	50	0	.	109532	3
	fir	249	72	38	100	144	60	862	37	297	65	996	44	795	42	1773	28	1428	24	2255	22	29	71	112	70	221	77	0	.	162	72	296	71	625	44	547	48	0	.	553	45	318	43	419	43	291	61	886	40	0	0	2652	25	378	39	544	51	242	58	361	69	622	54	527	37	545	45	769	43	147	76	193	63	567	39	711	46	44	76	560	49	191	55	406	43	184	59	435	36	280	62	96	59	202	54	893	45	446	38	501	42	287	100	128	76	176	100	18	100	208	52	167	100	0	.	0	.	0	.	407	53	276	60	251	70	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	334	53	542	39	171	55	66	100	0	.	131	92	28	58	78	100	19	100	32	99	0	.	35423	5
	pine	0	.	21	71	21	100	74	100	0	.	0	.	319	76	50	100	309	43	401	55	8	100	53	83	145	54	0	.	21	100	43	74	0	.	163	98	67	100	0	.	245	45	480	38	46	100	584	42	1135	28	383	37	18	100	152	72	0	.	50	100	29	100	0	.	0	.	44	100	0	.	176	100	42	100	41	100	0	.	58	60	0	.	0	.	302	41	64	81	97	53	4	71	119	51	69	83	8	100	0	.	12	100	100	89	165	79	0	.	46	74	89	79	0	.	4	100	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	339	34	594	35	0	.	25	100	119	59	49	100	70	61	76	66	32	100	0	.	0	.	8391	9
	larch	3	252	33	83	106	71	95	81	0	.	0	.	57	72	0	.	90	71	0	.	0	.	0	.	15	100	0	.	0	.	57	100	0	.	6	100	0	.	50	100	357	38	451	40	238	55	2131	18	5213	13	22	100	0	.	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	0	.	223	59	45	100	0	.	23	73	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	0	.	0	.	489	46	545	34	332	39	436	41	0	.	81	79	1655	23	551	35	594	56	0	.	0	.	0	.	242	45	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	0	.	3395	15	1628	23	1009	25	40	100	0	.	9	100	18	100	0	.	23	71	26	100	101	58	0	.	20760	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	257	47	1467	20	0	.	0	.	0	.	0	.	0	.	0	.	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	.	613	31	198	45	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2682	14
	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	1	100	5	100	0	.	8	75	26	42	7	100	74	100	274	87	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	0	.	0	.	1	100	5	100	0	.	0	.	11	100	6	100	40	77	0	.	5	100	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	.	1378	30
	beech	783	27	703	34	688	34	763	28	52	51	136	65	723	35	982	29	2367	19	2966	17	309	47	633	32	728	37	0	.	261	69	215	55	643	36	220	72	0	.	777	34	288	44	403	33	25	61	666	37	0	.	2532	19	726	36	423	62	8	81	119	53	61	40	226	39	99	51	88	42	139	80	53	60	1088	35	487	56	186	59	635	38	720	39	415	52	1787	23	1193	35	744	31	755	37	800	32	537	34	273	78	839	42	215	55	162	57	356	61	0	.	157	65	0	.	891	31	481	46	324	45	1021	29	1120	30	548	43	173	50	0	.	194	57	501	51	98	74	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	.	26	83	621	36	148	60	387	45	386	52	12	72	359	42	289	47	177	41	242	40	0	.	43135	4
	maple	233	32	158	39	53	82	14	155	0	.	96	57	227	38	242	45	310	27	483	26	96	56	90	67	178	52	0	.	60	74	17	74	149	42	26	100	3	100	206	30	92	50	56	70	37	68	119	43	0	.	336	32	103	41	20	91	84	46	76	71	80	47	83	64	158	79	26	59	73	55	35	78	135	80	61	66	62	72	266	45	79	84	255	51	97	55	164	46	168	42	65	43	156	46	136	44	53	52	38	58	96	56	65	53	16	75	36	96	101	72	11	100	18	73	0	.	32	72	22	42	90	72	28	83	57	69	17	79	3	77	68	50	75	52	105	58	37	66	2	100	132	48	10	100	31	63	25	60	66	42	63	64	96	52	38	71	10	100	0	.	60	75	0	.	17	92	245	41	86	52	164	51	14	131	66	55	69	58	243	55	53	72	166	64	0	.	8284	6
	ash	258	45	185	67	32	83	64	98	0	.	108	100	32	100	80	56	247	50	43	58	137	82	18	60	100	66	0	.	124	87	192	87	315	58	84	60	0	.	48	53	17	100	10	61	72	59	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	122	45	30	80	34	88	153	52	103	54	56	58	73	58	8	100	153	59	42	86	73	100	82	54	7	100	93	74	18	100	211	100	49	96	15	82	8	100	35	127	130	61	1	100	27	79	26	64	52	70	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	374	55	41	78	54	52	76	54	173	65	38	70	99	77	0	.	6536	10
	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	.	36	100	0	.	154	48	8	100	78	79	0	.	34	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	52	89	2	100	17	71	0	.	140	50	13	100	11	100	31	86	130	62	0	.	0	.	0	.	25	93	69	77	152	79	0	.	5	100	0	.	28	66	0	.	113	77	71	76	81	55	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	24	53	79	52	12	100	17	100	14	100	26	100	98	85	157	45	95	62	81	76	0	.	3620	11
	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	.	403	41	129	84	229	60	22	100	372	63	231	53	69	71	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	.	2076	19
	other broadleaves	118	44	133	37	140	44	80	81	0	.	12	75	60	51	98	45	437	32	93	34	88	57	40	71	35	52	0	.	0	0	158	74	39	60	141	58	13	71	72	59	90	46	173	59	395	37	561	34	341	63	265	43	46	51	36	93	17	71	0	.	13	75	7	100	-2	100	24	60	47	48	6	76	39	47	0	.	59	57	128	43	108	85	20	86	37	51	94	69	5	75	151	52	2	100	121	60	46	68	43	93	110	94	60	63	168	71	119	54	397	51	129	71	293	38	200	44	283	38	349	35	157	37	352	39	32	69	102	69	7	86	47	76	306	92	58	80	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	.	152	40	116	40	333	28	7	63	156	62	0	0	52	54	21	59	27	52	105	73	81	73	0	.	9418	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	5007	21	5530	18	7283	14	8249	14	790	37	1239	33	1835	32	252	.	1377	39	2204	29	3301	23	2900	29	162	80	5099	20	6712	16	5263	17	6190	17	15017	10	13991	10	8320	15	1871	27	2390	32	1324	36	826	42	1576	36	1251	36	1192	39	1538	33	1548	42	1892	31	3832	22	1714	36	1617	32	3887	22	2432	31	2294	34	3806	21	2982	25	1661	29	1505	28	1707	30	3629	24	2395	27	2503	29	1008	44	887	37	1476	34	1768	29	5376	28	2373	24	1963	21	760	34	1240	32	4384	16	2556	21	2650	24	1385	36	937	38	2120	30	2196	31	2460	30	1153	37	1057	39	363	59	3025	24	592	59	809	34	2181	24	1900	30	2241	26	1540	30	922	41	936	37	544	60	302	87	8716	13	6262	16	9372	13	1217	35	1545	33	1023	33	718	43	1279	31	1075	33	972	32	1150	34	0	.	251235	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	.	235	65	1516	43	1177	42	13607	11	3391	18	3056	21	752	35	0	.	0	.	125	78	0	.	35	100	1464	39	1268	34	776	44	3	100	234	58	2688	26	913	50	2379	30	1260	36	460	68	597	37	710	36	1129	31	2916	23	178	100	802	33	153	72	200	75	1131	36	907	41	193	.	725	55	913	39	669	63	827	40	706	53	2045	28	306	51	0	.	0	.	0	.	68	70	111	54	843	51	377	59	811	40	474	44	478	49	58	.	23	100	62	88	0	.	37	100	30	100	36	100	0	.	0	.	299	59	985	58	285	54	242	59	545	49	1192	33	225	83	108	73	271	52	0	.	0	.	553	43	189	100	12	100	146	85	37	97	88	100	0	.	182	89	692	42	821	30	948	35	365	71	397	55	268	68	1072	37	1301	35	74	106	13	100	230	59	0	.	65559	5
	fir	0	0	2	100	0	.	103	68	312	50	556	63	1485	29	4787	17	3395	22	541	35	3	100	24	100	159	49	0	.	185	100	71	71	321	79	53	59	4	100	2	100	65	100	10	100	0	.	117	100	0	.	1237	33	1200	37	2658	22	1321	29	161	75	545	44	29	100	51	100	982	48	221	79	0	.	499	69	309	63	50	100	158	64	576	59	1237	28	0	.	78	100	0	.	44	71	291	48	0	.	245	73	78	69	250	49	207	43	329	70	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	85	58	26	100	73	102	190	68	11	100	810	53	76	95	75	100	36	71	0	.	0	.	6	100	1	100	34	100	159	88	0	.	0	.	0	.	200	68	1	100	207	87	396	59	227	65	74	73	265	53	896	34	394	36	16	100	0	.	54	72	0	.	29300	6
	pine	26	100	0	.	0	.	0	.	0	.	0	.	73	52	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	.	40	100	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	.	2186	15
	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	.	88	63	100	81	43	124	466	43	317	65	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	.	258	71	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	2168	37	1061	35	633	55	0	.	30	100	0	.	2	100	0	.	0	.	0	.	76	100	0	.	5649	18
	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	136	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	193	44
	other conifers	0	.	0	.	0	.	0	.	0	.	14	74	44	79	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	.	846	46
	beech	440	58	115	66	0	.	1112	47	270	57	358	44	2154	31	2328	23	1930	22	607	39	0	.	252	90	603	40	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	567	44	243	43	1419	31	371	54	547	61	50	.	152	81	572	47	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	32	100	93	71	142	85	103	68	104	65	456	59	71	73	364	50	1	100	0	.	26	72	20	100	164	86	237	85	196	73	145	71	0	.	210	76	0	.	4	100	270	60	166	63	111	70	130	59	874	33	522	39	457	68	175	74	189	52	0	.	31485	6
	maple	133	63	24	100	0	.	20	64	25	75	170	57	671	23	232	34	88	49	228	47	45	95	27	73	49	47	0	.	0	.	65	61	195	47	64	68	0	.	225	60	70	62	95	93	51	100	0	.	3	100	172	39	107	46	31	60	216	41	24	73	134	51	0	.	0	.	18	73	81	63	0	.	99	73	113	72	191	64	138	54	164	54	476	32	3	100	4	100	6	.	35	76	133	72	60	72	56	77	49	97	218	74	-11	249	92	59	0	.	0	.	0	.	28	100	22	100	7	87	0	.	2	100	5	100	96	60	45	100	0	.	117	70	131	52	69	60	0	.	7	100	26	68	0	.	0	.	13	100	13	75	17	100	14	100	0	.	0	.	0	.	12	100	11	88	39	80	172	38	24	100	48	76	49	60	286	32	43	86	0	.	17	100	82	88	0	.	6483	8
	ash	0	.	194	59	0	.	9	100	0	.	146	60	155	74	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	328	41	78	91	35	100	0	.	103	77	127	52	180	69	4	100	169	89	198	46	255	59	145	57	0	.	0	.	0	.	31	59	27	77	171	63	0	.	24	100	84	54	135	86	5	100	0	.	119	68	112	61	18	100	320	65	85	95	0	.	1	100	0	.	0	.	0	.	68	100	67	100	68	66	29	100	0	.	70	71	60	68	134	63	35	70	37	100	66	85	7	100	147	50	150	50	0	.	32	100	20	100	0	.	8150	10
	oak	158	89	80	70	0	.	233	64	0	.	0	.	27	71	0	0	340	31	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	.	27	77	124	67	361	38	15	100	56	72	29	62	50	100	0	.	36	100	36	100	0	.	0	.	97	100	0	.	0	.	75	100	0	.	0	.	202	73	25	100	5	100	163	63	0	.	0	.	41	94	33	82	91	80	35	65	0	.	0	.	36	71	15	100	0	.	19	100	15	100	11	100	0	.	4252	15
	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	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3529	17
	other broadleaves	153	73	42	96	0	.	19	75	0	.	63	82	586	27	116	34	146	44	17	76	9	100	13	86	22	100	0	.	23	87	73	65	42	61	45	70	1	100	84	70	54	75	12	78	26	71	83	46	174	74	128	52	97	55	27	65	90	51	0	.	17	67	0	.	0	.	0	.	44	91	0	.	5	100	3	100	12	80	41	73	28	100	137	37	2	100	0	.	0	.	73	100	53	74	34	57	17	73	55	93	144	67	79	58	28	58	0	.	51	100	0	.	54	72	361	45	957	31	169	89	176	60	212	63	34	68	41	100	30	78	6	100	6	100	36	62	7	100	13	100	92	97	175	100	0	.	4	100	0	.	27	92	100	100	15	100	40	100	0	.	13	100	233	41	305	52	70	49	0	.	16	89	161	64	22	91	0	.	15	78	26	85	32	94	0	.	6418	9
	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	.	1732	36	2123	39	2483	33	18939	10	11284	13	9796	15	2191	27	94	71	350	84	1083	37	0	.	317	78	2486	33	2419	28	2391	28	323	71	916	41	3421	23	1434	39	2662	28	2196	28	1121	41	3643	23	3442	25	4504	21	5982	19	567	56	1607	32	250	71	370	68	2390	31	1790	32	193	.	1561	47	2489	34	1872	41	1732	33	1827	41	5696	20	937	42	699	59	56	.	408	74	1272	37	502	57	1211	43	968	46	2149	29	1723	35	1728	28	90	.	111	64	486	59	300	55	1813	27	2801	21	696	53	448	53	1630	29	1269	43	1174	52	562	54	851	43	908	42	2229	35	1181	54	374	68	789	42	252	76	0	.	603	43	436	62	348	69	727	51	479	52	302	71	0	.	796	54	3644	26	2898	23	2657	26	819	55	742	53	1037	44	3404	25	2539	28	658	52	332	70	752	42	0	.	164049	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	904	38	2795	30	2096	30	16396	10	5628	14	4669	16	2758	17	45	81	292	58	431	49	0	.	775	46	2690	26	2747	24	2458	29	4	75	3623	23	7954	15	4116	19	7457	16	10614	12	6245	14	2239	23	1225	30	1925	24	3879	20	317	62	1557	26	516	46	547	40	1718	29	1721	34	1623	30	2551	26	1201	32	1768	34	2719	25	1942	34	3209	24	1388	28	904	43	294	44	271	54	359	50	1825	28	2371	26	1324	32	977	36	769	35	826	38	1146	37	3326	38	1521	30	54	100	82	71	30	100	454	50	212	63	612	37	932	37	1750	38	1777	30	1215	44	1911	28	1451	29	918	39	137	62	1222	31	138	68	253	51	1994	22	855	38	782	40	732	40	166	65	300	47	32	75	425	68	4531	18	4397	18	6532	15	1039	37	754	37	694	37	1382	31	1858	29	84	94	439	50	578	38	0	.	175091	2
	fir	249	72	40	96	144	60	965	34	609	41	1551	36	2280	24	6560	14	4822	17	2797	19	32	64	136	60	380	49	0	.	347	63	366	59	946	40	600	44	4	100	555	44	383	40	429	42	291	61	1003	38	0	0	3889	20	1578	30	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	717	41	768	47	1643	24	184	59	514	34	280	62	140	46	493	36	893	45	692	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	407	53	276	60	251	70	549	42	26	100	313	55	479	37	556	55	1130	40	202	52	75	100	1054	34	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	541	46	938	34	398	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	64723	4
	pine	26	100	21	71	21	100	74	100	0	.	0	.	393	63	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	264	42	576	34	98	71	639	38	1199	27	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	58	60	0	.	53	100	330	39	100	63	97	53	4	71	119	51	69	83	8	100	0	.	85	68	117	77	202	66	0	.	46	74	89	79	0	.	4	100	0	.	0	.	0	.	12	100	0	.	40	100	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	436	29	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10577	8
	larch	30	94	55	64	106	71	95	81	0	.	0	.	181	57	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	445	33	551	36	281	50	2597	16	5530	13	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	224	59	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	489	46	545	34	591	38	436	41	0	.	86	75	1655	23	551	35	594	56	0	.	0	.	0	.	242	45	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	5564	17	2689	19	1642	26	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	26409	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	262	46	1467	20	0	.	0	.	0	.	0	.	0	.	0	.	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	.	653	30	334	36	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2875	13
	other conifers	42	75	0	.	71	100	85	72	0	.	14	74	44	79	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	8	93	5	100	0	.	8	75	26	42	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	5	100	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	.	2224	25
	beech	1223	27	818	30	688	34	1875	30	323	48	494	36	2877	25	3310	18	4298	14	3573	16	309	47	885	34	1331	27	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	611	32	25	61	719	35	0	.	3416	17	1551	26	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1202	28	964	31	1834	27	2159	21	1740	31	794	30	907	34	1372	27	634	31	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	891	31	1326	30	743	34	1260	30	1120	30	548	43	743	42	32	100	287	45	643	44	201	50	445	45	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	.	30	74	890	31	314	43	498	38	515	42	886	33	881	28	746	45	352	42	432	32	0	.	74620	3
	maple	366	30	182	36	53	82	34	74	25	75	266	42	898	20	474	28	399	24	711	23	140	49	117	54	227	42	0	.	60	74	82	51	344	32	91	56	3	100	431	34	162	39	151	64	88	65	119	43	3	100	508	24	210	31	51	51	301	32	100	57	214	36	83	64	158	79	45	46	153	42	35	78	234	56	174	52	253	51	404	35	244	45	731	27	101	53	168	45	174	40	99	39	289	41	196	37	109	47	87	60	314	54	54	81	108	51	36	96	101	72	11	100	46	67	22	100	39	61	22	42	92	70	32	73	153	46	62	75	3	77	184	48	206	38	175	43	37	66	9	78	158	42	10	100	31	63	38	52	79	37	80	55	109	47	38	71	10	100	0	.	71	65	11	88	56	62	416	29	110	46	212	43	64	55	351	28	111	48	243	55	70	60	248	51	0	.	14767	5
	ash	258	45	380	44	32	83	74	87	0	.	254	55	187	64	451	46	645	31	89	48	137	82	34	56	172	51	0	.	166	70	409	67	484	42	284	38	9	100	262	44	60	77	10	61	182	54	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	143	41	138	80	363	38	231	46	138	48	56	58	176	51	135	49	333	46	46	79	242	69	279	36	262	58	238	45	18	100	211	100	49	96	46	48	35	64	205	56	130	61	25	95	111	45	161	73	57	64	0	.	181	51	144	51	66	61	320	65	97	84	24	74	1	100	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	113	49	136	62	236	45	115	60	440	49	48	68	201	39	226	38	173	65	71	59	119	66	0	.	14686	7
	oak	349	55	80	70	0	.	391	48	0	.	0	.	33	61	68	73	602	22	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	36	100	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	2	100	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	5	100	0	.	54	51	124	67	474	33	85	65	137	44	102	61	50	100	0	.	36	100	50	74	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	115	64	113	41	12	100	17	100	50	58	42	73	98	85	176	41	110	55	92	68	0	.	7872	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	415	40	294	67	390	47	419	35	1190	31	468	52	257	56	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	.	134	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5605	12
	other broadleaves	271	45	174	36	140	44	98	67	0	.	75	69	646	25	213	27	583	26	110	31	97	52	54	57	57	50	0	.	23	87	231	55	81	42	186	47	14	65	155	46	144	40	184	55	421	35	644	31	515	49	393	34	144	41	63	60	107	45	0	.	30	50	7	100	-2	100	24	60	91	51	6	76	45	43	3	100	71	49	169	37	136	70	157	34	39	48	94	69	5	75	224	48	55	71	155	48	63	54	99	66	254	55	140	43	197	61	119	54	448	47	129	71	346	34	561	33	1240	26	518	37	333	36	565	34	66	48	143	57	37	65	53	69	312	91	95	55	40	50	194	84	147	64	178	98	4	75	24	53	116	71	47	60	118	86	46	66	122	75	5	100	13	100	385	29	421	39	404	24	7	63	172	57	161	64	74	46	21	59	42	44	131	61	114	59	0	.	15836	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	4596	20	3751	28	4750	24	23946	9	16814	10	17079	10	10439	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5719	17	5291	20	485	54	6015	18	10133	13	6697	16	8852	15	17213	9	15112	10	11963	12	5313	19	6895	17	7305	16	1393	33	3184	23	1501	32	1562	34	3928	22	3339	26	2086	29	5392	21	4202	25	3489	26	5619	18	4259	25	7990	17	4744	18	3681	23	1717	28	1913	27	2980	23	4130	22	3607	23	3470	24	3157	24	2610	26	3204	21	1857	28	5487	28	2859	22	2264	20	2573	21	4041	17	5080	15	3004	19	4280	19	2654	28	2111	34	2682	26	3047	25	3368	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12360	12	9160	13	12028	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	415285	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. 2276643. Birmensdorf, Swiss Federal Research Institute WSL

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