Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
growing stock (stemwood)
protection forest (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
protection forest (2022)	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	.
inside	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	.	1349	40	1184	42	11139	13	3869	18	346	49	582	34	0	.	40	92	107	100	0	.	251	91	1972	32	1236	38	974	44	0	.	1981	33	4809	19	2185	24	4526	21	7323	15	3658	19	507	44	113	66	0	.	1263	37	63	.	188	62	48	.	50	95	296	79	705	48	859	42	1394	30	226	47	466	60	2177	27	610	50	1377	33	182	71	0	.	117	.	0	.	10	100	911	41	1087	39	562	45	283	52	0	.	25	100	1044	38	1930	29	1614	31	12	100	64	71	24	100	322	55	9	100	408	44	650	52	829	55	1364	39	653	68	921	45	1024	37	235	74	35	.	6	100	0	.	0	.	0	.	0	.	0	.	1	.	0	.	181	94	108	.	15	.	3160	20	3847	19	5476	17	276	58	204	63	261	69	307	47	339	68	0	.	168	.	0	.	0	.	86537	4
	fir	0	.	0	.	0	.	0	.	206	55	904	46	1438	31	3502	19	922	40	1309	31	0	.	0	.	31	93	0	.	166	100	114	72	685	42	270	66	0	.	268	67	194	45	292	53	27	100	712	41	17	100	1119	34	197	64	0	.	279	66	0	.	86	55	61	.	153	100	369	59	319	56	57	100	648	48	469	49	133	60	464	46	404	56	869	36	0	.	0	.	6	.	0	.	43	100	604	47	485	45	353	43	184	59	26	100	0	.	9	100	126	54	0	.	0	.	0	.	27	100	367	55	260	58	171	69	528	48	0	.	328	51	278	47	430	57	817	42	44	100	0	.	58	89	125	.	0	.	0	.	0	.	0	.	7	.	0	.	33	100	60	.	62	.	125	89	471	50	646	39	88	67	0	.	67	100	306	51	182	57	0	.	0	.	0	.	0	.	24001	7
	pine	0	.	0	.	0	.	0	.	0	.	0	.	105	84	31	86	0	.	65	65	0	.	0	.	45	100	0	.	0	.	0	.	0	.	25	100	0	.	0	.	57	73	321	46	0	.	217	43	559	47	115	59	16	100	0	.	0	.	0	.	27	100	0	.	0	.	24	100	0	.	0	.	16	100	0	.	0	.	105	72	0	.	43	100	9	100	0	.	0	.	11	100	0	.	0	.	0	.	0	.	25	73	0	.	0	.	0	.	64	78	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	184	62	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	668	38	305	31	502	38	0	.	30	100	96	73	35	72	0	.	0	.	27	.	0	.	0	.	3744	13
	larch	0	.	0	.	0	.	0	.	0	.	0	.	41	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	209	60	319	53	27	93	1469	21	2595	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	198	62	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	9	100	0	.	0	.	474	49	412	36	379	46	312	48	0	.	67	88	941	28	392	42	518	60	0	.	0	.	0	.	50	93	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3797	21	1859	22	1195	24	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15431	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	102	74	602	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	408	38	305	36	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1443	19
	other conifers	0	.	0	.	0	.	0	.	0	.	6	100	38	96	0	.	71	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	7	100	5	63	-5	145	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	3	100	-12	114	-16	100	7	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	5	100	1	100	0	.	0	.	2	100	33	74	6	100	0	.	0	.	0	.	0	.	223	45
	beech	0	.	0	.	0	.	0	.	123	67	234	46	2084	29	2122	22	626	45	1203	27	50	.	295	63	485	48	0	.	11	100	229	55	686	36	2	100	0	.	489	38	603	51	258	43	13	100	546	40	0	.	1623	24	423	45	0	.	626	46	69	.	40	69	20	.	10	100	67	54	367	46	14	100	813	37	150	46	952	46	736	35	390	46	1068	34	197	72	0	.	0	.	132	111	259	71	387	36	260	85	741	45	114	46	128	71	176	100	0	.	131	63	0	.	692	36	1128	35	593	35	946	32	855	32	465	41	336	66	0	.	231	56	231	62	131	53	401	45	212	64	19	.	76	90	115	.	3	.	0	.	0	.	0	.	3	.	0	.	80	86	72	.	15	.	0	.	20	74	803	32	217	50	218	64	54	96	250	44	187	47	0	.	23	.	0	.	0	.	28328	6
	maple	0	.	0	.	0	.	0	.	8	100	67	67	759	23	142	36	6	100	290	40	0	.	61	78	46	96	0	.	0	.	41	78	200	46	19	100	0	.	279	34	137	49	167	91	34	100	116	46	0	.	182	32	77	54	0	.	124	42	0	.	39	61	37	.	11	72	14	74	72	45	24	76	218	65	108	52	10	417	351	40	205	49	400	34	0	.	0	.	0	.	14	100	27	78	95	54	77	49	35	69	161	81	11	100	-12	100	12	100	70	73	19	100	2	100	32	100	40	52	3	70	36	79	0	.	56	61	4	100	0	.	174	57	76	52	124	41	0	.	0	.	48	72	0	.	2	.	0	.	0	.	33	100	0	.	0	.	16	100	0	.	9	.	0	.	35	69	209	37	105	53	99	76	-19	226	123	49	23	101	0	.	0	.	0	.	0	.	5979	8
	ash	0	.	0	.	0	.	0	.	0	.	158	72	226	57	389	55	99	72	66	64	61	.	15	100	4	100	0	.	0	.	252	69	129	58	30	95	0	.	237	48	8	100	6	230	57	80	78	70	0	.	349	51	124	57	0	.	64	64	0	.	29	71	0	.	2	100	0	.	82	54	0	.	66	72	191	52	164	55	76	52	21	100	308	46	0	.	0	.	0	.	80	99	35	100	267	53	102	97	272	66	124	69	0	.	43	100	0	.	123	100	38	100	38	51	30	64	49	90	75	68	70	61	85	49	35	56	47	67	0	.	169	72	26	90	56	67	156	79	39	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	97	100	0	.	24	.	48	57	32	97	220	50	62	81	156	83	22	96	95	56	12	100	0	.	17	.	0	.	0	.	6046	10
	oak	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	60	61	0	.	0	.	0	.	26	100	0	.	0	.	54	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	20	100	72	100	21	71	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	27	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	33	95	40	100	17	100	0	.	8	100	0	.	50	50	112	62	349	37	102	64	127	41	83	75	10	100	0	.	31	100	15	72	25	100	0	.	70	88	0	.	33	100	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	71	80	83	31	61	0	.	0	.	27	71	11	100	0	.	0	.	0	.	0	.	0	.	1608	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	.	319	57	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	229	59	251	67	388	52	303	38	1011	31	426	50	330	47	1510	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4932	13
	other broadleaves	0	.	0	.	0	.	0	.	3	100	48	95	692	37	114	32	5	100	43	44	0	.	8	100	13	92	0	.	4	100	6	71	10	71	35	60	0	.	142	55	77	54	73	47	247	40	633	31	258	51	75	39	13	70	0	.	20	105	0	.	2	100	0	.	12	100	4	100	48	72	15	68	22	64	0	.	56	51	121	38	-11	100	97	42	0	0	0	.	1	.	77	79	13	100	74	59	4	75	37	143	0	.	40	100	9	100	110	66	146	39	106	76	255	41	319	47	695	30	269	32	349	49	354	40	47	65	44	100	22	84	72	89	12	70	55	55	6	100	0	.	0	.	0	.	0	.	0	.	0	.	128	100	0	.	0	.	0	0	4	.	0	.	265	38	249	41	171	45	7	100	23	71	0	.	31	68	0	.	0	.	0	.	0	.	0	.	6883	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
	total	0	.	0	.	0	.	0	.	1689	37	2600	33	16521	11	10168	14	2136	31	3560	22	112	.	419	54	757	48	0	.	432	71	2668	29	2948	26	1355	38	0	.	3408	26	6111	17	3622	21	4932	19	11541	12	7808	14	3986	20	965	37	0	.	2376	30	132	.	411	54	166	.	240	91	774	48	1593	37	969	40	3181	25	1146	38	1825	33	4213	21	1627	38	4172	23	387	71	0	.	124	.	314	63	387	58	2354	30	2014	30	2055	32	933	45	245	72	261	73	1680	29	3239	22	2408	24	1748	23	1989	26	2856	19	3502	17	2428	22	3611	21	1662	35	925	51	2167	32	1642	36	1621	35	2487	32	723	62	92	.	221	76	240	.	34	.	0	.	0	.	169	79	11	.	0	.	407	83	244	.	126	.	8521	14	7208	14	9334	14	811	41	731	47	508	55	1191	36	749	45	0	.	234	.	0	.	0	.	185155	2
outside	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	384	37	508	42	801	45	1536	33	1177	44	1691	35	5466	20	2823	21	5335	17	2825	19	3	100	275	72	320	57	0	.	565	51	907	40	1502	33	782	37	0	.	2067	31	2287	28	1278	28	2672	26	2804	24	2366	21	2265	25	1415	33	2064	26	3772	25	325	60	1091	32	620	45	480	39	1461	32	749	45	642	48	643	47	643	48	1025	42	369	70	1459	40	2237	35	1707	27	895	45	449	55	314	67	308	52	1362	35	1005	39	837	42	557	47	973	38	1335	30	15	100	234	59	433	89	0	.	0	.	0	.	14	100	71	71	-9	100	208	60	650	52	396	55	383	41	654	34	345	48	696	45	153	59	1136	30	96	51	342	56	2139	21	812	38	947	32	1126	36	325	58	244	65	26	100	345	71	941	41	383	49	440	40	540	49	916	41	388	42	934	37	1499	31	768	56	541	53	586	34	0	.	91089	4
	fir	388	46	102	72	119	72	1278	33	351	51	637	58	379	42	2727	23	3753	19	1731	31	18	73	120	61	288	57	0	.	97	82	211	75	480	62	418	69	0	.	257	72	103	59	194	68	254	73	197	100	0	.	2449	25	1281	34	2979	21	1117	28	529	53	1060	39	450	38	433	45	982	41	12	62	112	79	250	61	393	50	0	.	13	100	145	54	729	40	139	57	488	39	372	57	208	48	199	39	42	81	256	61	214	65	351	65	271	44	466	57	0	.	0	.	162	100	0	.	0	.	0	.	0	.	0	.	0	.	74	77	25	100	22	100	181	59	15	100	138	100	43	72	65	88	879	39	108	90	342	45	353	49	393	49	477	35	369	47	361	68	375	57	298	82	104	92	0	.	16	96	53	88	283	64	174	50	236	55	673	38	232	50	56	82	25	100	185	82	0	.	36761	5
	pine	76	79	35	71	29	76	72	87	0	.	17	100	340	87	31	100	497	32	353	62	47	100	9	292	117	49	0	.	18	100	168	79	0	.	283	65	110	79	0	.	154	52	248	55	48	100	387	49	639	40	254	46	0	.	170	56	0	.	107	100	0	.	0	.	0	.	0	.	0	.	147	100	0	.	31	100	0	.	0	.	0	.	0	.	351	39	99	64	104	52	-6	100	122	49	47	81	8	100	0	.	64	77	159	55	193	59	0	.	0	.	85	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	0	.	0	.	0	.	0	.	0	.	26	71	10	100	0	.	28	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	234	52	96	74	215	74	0	.	0	.	124	64	61	81	250	42	169	53	159	74	45	100	0	.	7230	10
	larch	14	73	75	68	67	98	144	69	0	.	0	.	103	75	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	46	100	0	.	25	100	0	.	57	100	202	49	200	56	178	69	683	27	2512	22	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	14	100	12	100	0	.	11	100	45	100	0	.	39	66	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	46	100	0	.	37	100	79	69	86	69	60	73	0	.	18	100	263	45	65	61	35	71	0	.	0	.	0	.	125	47	0	.	0	.	0	.	46	100	0	.	13	100	0	.	0	.	12	100	0	.	49	71	0	.	11	100	0	.	29	100	1287	31	589	37	192	62	0	.	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	8044	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	2	100	121	55	678	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	45	30	86	82	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1046	20
	other conifers	36	77	0	.	43	100	42	69	0	.	2	100	0	.	0	.	291	52	1	100	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	0	.	8	94	0	.	0	.	3	100	23	50	0	.	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	291	100	0	.	0	0	0	.	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	103	100	17	62	0	.	0	.	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	7	100	0	.	1	100	0	.	0	.	12	90	0	.	0	.	0	.	0	.	0	.	0	.	1590	30
	beech	1448	27	816	31	651	32	2113	30	135	62	323	48	760	54	781	27	3822	15	2344	20	352	49	689	39	1423	35	0	.	258	66	527	42	332	58	1258	35	0	.	472	47	77	60	333	43	21	87	19	100	0	.	1971	23	940	35	823	34	629	43	359	55	178	41	211	40	215	41	368	39	69	99	21	71	690	53	420	49	55	53	217	62	701	46	573	42	1911	23	1364	30	1059	27	945	36	1177	30	137	53	0	.	220	59	441	37	278	49	586	29	0	.	0	.	0	.	73	79	72	61	24	100	3	100	22	100	0	.	283	55	25	100	0	.	352	62	57	100	0	.	246	71	56	78	1026	31	14	71	107	50	308	33	383	43	1115	32	505	58	578	46	97	71	198	58	248	66	0	.	0	.	135	80	55	71	197	46	365	48	646	38	720	35	708	46	426	45	406	33	0	.	44958	4
	maple	272	38	131	43	108	47	71	45	10	100	178	67	141	64	183	47	203	34	301	29	128	59	40	50	212	45	0	.	73	71	13	65	102	54	36	63	0	.	85	50	0	5384	5	100	32	72	0	.	0	.	264	30	75	49	68	44	182	46	76	56	136	52	9	95	89	93	19	59	73	47	0	.	42	73	79	68	45	72	0	.	17	81	211	49	86	52	116	47	146	36	62	54	191	42	105	56	21	72	17	73	104	53	90	61	86	65	0	.	0	.	0	.	19	100	16	100	2	100	8	63	0	.	6	77	92	59	33	100	0	.	16	72	142	56	0	.	27	64	0	.	87	51	0	.	13	92	28	56	45	65	53	54	81	48	13	100	0	.	0	.	-1	442	2	100	3	100	74	43	17	100	76	58	48	71	160	47	80	57	201	55	132	61	235	55	0	.	6437	7
	ash	274	37	415	42	170	58	158	65	0	.	177	65	0	6096	122	51	565	30	52	89	218	65	29	67	184	50	0	.	198	62	109	94	205	54	366	46	28	100	61	80	38	100	2	100	115	57	11	100	0	.	573	32	491	90	197	50	53	77	75	64	14	100	2	100	2	100	4	100	357	69	0	.	7	83	331	50	147	69	34	100	177	68	68	78	215	42	102	47	52	63	174	56	59	62	71	78	0	.	0	.	293	46	429	42	177	45	11	100	0	.	0	.	15	100	8	100	109	83	27	100	0	.	0	.	98	86	1	100	0	.	115	64	95	69	0	.	14	100	13	58	41	58	71	99	2	100	9	100	23	92	104	63	230	60	52	65	115	97	0	.	212	87	48	75	69	80	20	66	109	100	239	66	101	57	162	43	237	37	146	68	97	53	170	51	0	.	10361	8
	oak	287	60	62	72	0	.	346	49	0	.	0	.	22	71	60	71	545	24	0	.	79	100	3	100	89	67	0	.	8	100	0	.	33	77	64	74	473	55	0	.	0	.	59	100	0	.	0	.	0	.	278	51	92	92	180	67	36	100	51	75	0	.	33	100	0	.	0	.	5	100	0	.	0	.	32	100	0	.	0	.	23	70	0	.	241	52	0	.	20	70	18	95	164	45	0	.	0	.	0	.	85	58	446	76	272	45	0	.	0	.	0	.	1	100	8	74	96	76	0	.	0	.	0	.	34	100	19	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	176	72	70	70	0	.	405	49	20	100	26	62	253	47	25	100	23	100	25	100	6	100	16	74	50	53	10	100	51	81	15	100	20	100	126	64	155	43	182	59	81	66	0	.	6008	11
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	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	.	48	74	0	0	14	85	254	49	116	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	104	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	637	32
	other broadleaves	283	42	96	38	140	49	88	77	0	0	16	79	96	47	40	60	661	33	56	45	89	53	31	69	67	50	0	.	14	100	355	76	27	59	84	71	21	87	45	93	46	55	41	71	153	76	138	51	156	64	224	49	118	58	65	61	49	56	2	100	24	54	7	100	11	71	44	85	31	87	0	.	2	100	6	100	15	70	37	87	248	59	35	66	37	47	42	50	61	66	99	67	0	0	53	65	15	152	61	100	229	62	81	50	506	87	30	71	42	100	16	79	49	78	201	47	270	54	296	69	43	100	73	59	18	67	115	65	7	84	99	91	270	94	44	94	26	71	125	78	117	50	178	97	6	80	26	56	87	70	23	55	124	86	51	57	123	67	0	.	12	100	88	57	66	44	148	41	0	.	140	72	104	57	35	80	8	58	32	62	142	60	73	66	0	.	8146	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	0	0	.	0	0
	total	3462	19	2241	23	2133	26	5849	19	1673	42	3041	31	7308	19	6783	17	15822	10	7663	15	934	38	1196	38	2725	28	177	.	1230	39	2336	30	2681	26	3316	24	632	51	3044	27	2927	24	2359	24	3476	23	4450	19	6372	15	8298	15	4476	23	6759	18	5839	21	1524	33	2504	27	1339	35	1272	35	2879	25	1296	35	923	46	1647	36	2238	33	1287	38	680	49	2815	30	3854	28	4782	18	3113	24	2244	25	1852	31	2220	26	1816	29	1305	36	1348	39	2123	30	2773	30	3624	23	93	58	403	47	786	58	231	46	558	42	634	41	611	43	200	48	104	49	808	47	923	44	425	56	1271	36	1248	35	528	51	1053	42	586	47	3320	22	656	51	910	37	2863	20	2181	27	2738	24	2721	25	1633	35	990	40	640	66	974	49	2728	24	1268	31	1513	26	1014	46	1814	36	1407	29	2693	26	3152	24	2284	29	1724	32	1960	27	0	.	222307	2
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	384	37	508	42	801	45	1536	33	2526	29	2875	27	16604	11	6692	13	5681	16	3408	17	3	100	315	64	427	50	0	.	815	45	2879	25	2737	25	1757	29	0	.	4047	22	7096	16	3464	18	7198	16	10126	13	6023	14	2773	22	1529	31	2064	26	5036	21	388	52	1280	28	668	42	529	37	1757	29	1454	33	1501	31	2037	25	868	38	1490	34	2546	25	2069	32	3615	25	1888	25	895	45	566	48	314	67	318	51	2273	26	2092	27	1399	31	840	36	973	38	1361	29	1059	38	2164	27	2046	31	12	100	64	71	24	100	337	53	80	65	399	45	858	42	1480	38	1760	33	1036	46	1574	30	1369	30	931	38	187	51	1143	30	96	51	342	56	2139	21	812	38	947	32	1127	36	325	58	425	55	133	83	359	69	4101	18	4229	17	5916	16	816	38	1119	36	649	37	1241	30	1837	28	768	56	709	47	586	34	0	.	177626	2
	fir	388	46	102	72	119	72	1278	33	557	38	1541	36	1818	26	6228	15	4675	17	3040	22	18	73	120	61	318	53	0	.	263	70	325	55	1166	36	688	49	0	.	525	49	298	36	486	42	282	66	908	38	17	100	3568	20	1478	31	2979	21	1396	26	529	53	1146	36	511	35	587	42	1351	34	331	54	170	62	898	38	862	35	133	60	476	45	550	43	1598	27	139	57	488	39	378	56	208	48	242	37	645	44	741	36	567	36	535	47	298	41	466	57	9	100	126	54	162	100	0	.	0	.	27	100	367	55	260	58	171	69	602	43	25	100	350	48	459	36	446	55	956	39	86	62	65	88	937	37	233	68	342	45	353	49	393	49	477	35	376	46	361	68	408	53	358	70	166	69	125	89	487	49	699	36	371	51	174	50	302	48	979	31	414	38	56	82	25	100	185	82	0	.	60763	4
	pine	76	79	35	71	29	76	72	87	0	.	17	100	445	69	62	66	497	32	418	53	47	100	9	292	162	45	0	.	18	100	168	79	0	.	308	60	110	79	0	.	212	43	569	35	48	100	605	35	1199	30	369	37	16	100	170	56	0	.	107	100	27	100	0	.	0	.	24	100	0	.	147	100	16	100	31	100	0	.	105	72	0	.	43	100	360	39	99	64	104	52	6	231	122	49	47	81	8	100	0	.	89	59	159	55	193	59	0	.	64	78	85	75	0	.	0	.	0	.	0	.	0	.	15	100	0	.	54	100	184	62	0	0	0	.	0	.	0	.	26	71	10	100	0	.	28	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	902	31	401	29	717	34	0	.	30	100	220	48	97	58	250	42	169	53	186	65	45	100	0	.	10974	8
	larch	14	73	75	68	67	98	144	69	0	.	0	.	145	60	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	46	100	0	.	25	100	0	.	67	87	412	39	519	39	205	61	2152	17	5106	14	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	14	100	12	100	30	100	208	59	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	511	46	491	32	465	40	372	42	0	.	86	72	1204	24	457	37	553	56	0	.	0	.	0	.	175	43	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	12	100	0	.	49	71	0	.	11	100	0	.	29	100	5084	17	2447	19	1387	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	23475	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	73	0	.	2	100	223	45	1280	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	.	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	.	525	31	335	34	98	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2489	13
	other conifers	36	77	0	.	43	100	42	69	0	.	8	78	38	96	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	8	94	0	.	0	.	9	77	28	42	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-16	100	7	86	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	103	100	17	62	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	8	86	5	100	2	75	0	.	0	.	13	80	33	74	6	100	0	.	0	.	0	.	0	.	1813	27
	beech	1448	27	816	31	651	32	2113	30	258	46	557	33	2844	25	2903	17	4448	14	3547	16	403	45	984	33	1908	28	0	.	269	64	756	34	1018	31	1260	35	0	.	961	30	680	46	590	31	34	66	565	39	0	.	3594	16	1363	28	823	34	1255	31	428	49	219	35	231	37	224	40	435	34	436	42	36	58	1503	31	570	38	1007	44	953	31	1092	34	1642	26	2108	21	1364	30	1059	27	1077	34	1436	27	525	30	260	85	961	37	555	31	406	40	762	32	0	.	131	63	0	.	764	33	1200	33	617	34	949	31	877	31	465	41	619	43	25	100	231	56	583	45	188	48	401	45	458	48	74	64	1102	30	129	90	109	49	308	33	383	43	1115	32	507	57	578	46	176	55	270	50	264	63	0	.	20	74	937	30	273	43	414	40	418	44	896	30	907	29	708	46	449	43	406	33	0	.	73286	3
	maple	272	38	131	43	108	47	71	45	18	71	245	52	899	22	325	31	209	33	591	24	128	59	101	51	258	41	0	.	73	71	54	61	302	36	55	54	0	.	365	28	136	50	172	89	66	62	116	46	0	.	445	22	153	36	68	44	306	32	76	56	175	42	46	82	100	83	33	46	145	33	24	76	259	55	187	41	56	98	351	40	222	46	611	28	86	52	116	47	146	36	76	48	219	38	200	39	97	42	52	52	264	53	101	55	74	76	12	100	70	73	19	100	21	92	48	74	41	50	11	50	36	79	6	77	148	43	37	90	0	.	190	52	218	41	124	41	27	64	0	.	135	41	0	.	15	83	28	56	45	65	86	51	81	48	13	100	16	100	0	.	8	140	2	100	38	65	282	29	122	47	175	50	29	189	282	34	103	49	201	55	132	61	235	55	0	.	12416	5
	ash	274	37	415	42	170	58	158	65	0	.	335	48	226	57	511	43	664	28	118	53	280	55	44	56	189	49	0	.	198	62	361	56	334	40	395	43	28	100	297	42	46	85	7	183	172	46	89	63	0	.	922	27	615	73	197	50	117	49	75	64	43	58	2	100	5	70	4	100	439	57	0	.	73	65	522	37	311	43	110	47	198	62	376	40	215	42	102	47	52	63	254	50	94	54	337	45	102	97	272	66	416	38	429	42	220	41	11	100	123	100	38	100	53	46	38	55	158	64	102	57	70	61	85	49	133	65	49	65	0	.	284	50	121	57	56	67	171	73	51	77	41	58	71	99	2	100	9	100	23	92	112	58	230	60	52	65	212	69	0	.	236	79	96	47	101	63	240	46	171	70	395	52	122	50	257	34	249	35	146	68	113	47	170	51	0	.	16407	6
	oak	287	60	62	72	0	.	346	49	0	.	0	.	28	60	60	71	606	22	0	.	79	100	3	100	115	56	0	.	8	100	54	100	34	74	64	74	473	55	0	.	0	.	59	100	0	.	20	100	72	100	299	48	92	92	180	67	36	100	51	75	0	.	33	100	2	100	0	.	5	100	0	.	0	.	32	100	27	100	1	100	23	70	0	.	241	52	0	.	20	70	18	95	164	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	8	100	0	.	51	49	119	58	445	33	102	64	127	41	83	75	44	81	19	100	31	100	15	72	25	100	0	.	70	88	0	.	40	84	176	72	95	58	0	.	405	49	20	100	26	62	253	47	25	100	23	100	25	100	28	60	96	70	82	40	10	100	51	81	41	58	31	74	126	64	155	43	182	59	81	66	0	.	7615	9
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	406	49	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	277	50	251	67	402	50	556	30	1128	28	426	50	330	47	1510	31	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	26	100	0	.	168	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5569	12
	other broadleaves	283	42	96	38	140	49	88	77	3	100	63	75	789	33	153	28	666	33	99	32	89	53	40	58	81	44	0	.	18	80	361	74	37	47	119	53	21	87	187	47	123	40	114	39	400	38	772	27	414	40	299	38	131	52	65	61	69	50	2	100	26	50	7	100	23	63	48	78	79	55	15	68	24	60	6	100	71	43	158	35	237	62	132	35	37	47	42	50	62	65	175	51	13	100	128	44	19	123	98	82	229	62	121	47	515	85	140	54	188	37	122	67	304	36	521	34	965	26	565	39	392	45	427	35	66	51	158	54	29	67	171	65	282	89	99	52	32	61	125	78	117	50	178	97	6	80	26	56	87	70	151	85	124	86	51	57	123	67	4	100	12	100	353	32	315	34	319	31	7	100	163	63	104	57	66	53	8	58	32	62	142	60	73	66	0	.	15029	7
	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	0
	total	3462	19	2241	23	2133	26	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	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. 2297439. Birmensdorf, Swiss Federal Research Institute WSL

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