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 against hillslope debris flows/landslides (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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 against hillslope debris flows/landslides (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	.	121	91	602	55	1401	25	402	42	16	100	221	38	0	.	21	100	11	100	0	.	32	.	0	.	136	55	0	.	0	.	466	49	915	38	983	31	987	32	2710	22	1065	29	284	47	46	100	0	.	0	.	35	100	63	79	15	100	27	95	44	68	145	95	0	.	378	51	80	65	319	60	397	59	0	.	161	70	103	71	0	.	0	.	0	.	14	100	161	56	0	.	45	100	37	97	0	.	0	.	238	59	183	50	425	37	0	.	21	100	13	100	102	83	5	100	0	.	161	100	205	67	949	37	65	97	226	70	183	59	97	100	0	.	4	100	4	100	0	.	0	.	0	.	0	.	1	.	0	.	0	.	65	91	0	.	1214	26	1440	22	1906	24	14	100	0	.	0	.	5	.	5	.	0	.	0	.	0	.	0	.	19975	7
	fir	0	.	0	.	0	.	0	.	57	100	72	57	77	70	528	37	160	74	445	42	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	109	91	0	.	116	73	0	.	29	100	0	0	437	44	21	100	42	.	0	.	4	100	95	71	0	.	83	100	304	51	0	.	0	.	168	100	11	100	108	78	26	86	44	.	251	60	0	.	51	.	0	.	0	.	40	100	123	90	0	.	102	75	40	68	0	.	0	.	0	.	174	82	0	.	0	.	0	.	0	.	39	100	0	.	0	.	3	100	1	100	133	51	69	67	112	95	77	63	4	100	0	.	33	90	85	90	0	.	0	.	0	.	0	.	4	.	0	.	0	.	34	97	0	.	168	93	88	91	62	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4631	13
	pine	0	.	0	.	0	.	0	.	20	100	0	.	68	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	75	107	54	0	.	55	75	169	69	176	67	0	.	0	.	0	.	0	.	14	100	18	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	10	72	93	67	105	72	0	.	0	.	0	.	0	.	0	.	0	.	141	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	106	59	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	332	42	120	33	125	52	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	1798	17
	larch	0	.	0	.	0	.	0	.	6	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	62	107	77	18	84	250	42	245	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	107	72	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	75	16	63	32	60	12	100	0	.	5	100	119	54	0	0	0	.	0	.	0	.	0	.	25	100	0	.	51	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	651	24	605	30	460	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2817	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	72	110	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	64	69	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	299	31
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	2	100	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	60
	beech	0	.	0	.	0	.	0	.	7	100	259	83	887	39	398	41	141	69	641	30	27	.	139	72	134	72	0	.	0	.	0	.	193	53	0	.	0	.	288	48	55	65	109	45	8	100	6	100	0	.	480	31	195	59	0	.	0	.	156	59	16	88	82	100	37	87	40	52	106	61	0	.	31	71	18	57	306	63	179	54	54	.	155	56	150	60	13	.	0	.	213	58	267	54	109	47	0	.	206	80	26	60	0	.	0	.	0	.	142	67	0	.	329	75	266	51	178	54	65	71	104	64	44	77	0	.	0	.	121	63	129	62	81	62	149	60	266	52	0	.	43	90	83	85	0	.	0	.	0	.	0	.	2	.	0	.	0	.	82	71	0	.	0	.	0	.	343	38	121	78	0	.	0	.	22	.	7	.	0	.	0	.	0	.	0	.	8709	9
	maple	0	.	0	.	0	.	0	.	0	.	31	80	175	31	4	100	4	100	88	44	0	.	9	100	0	.	0	.	5	.	0	.	33	70	0	.	0	.	96	46	24	100	26	68	53	74	2	100	1	100	76	47	54	63	14	.	0	.	0	.	21	61	21	100	6	72	5	100	14	71	0	.	2	100	43	72	-11	201	45	73	12	.	73	51	3	100	5	.	0	.	6	100	13	76	29	87	0	.	8	93	13	71	0	.	0	.	0	.	0	.	0	.	1	100	0	.	50	49	9	100	7	100	0	.	0	.	5	100	0	.	27	70	36	60	66	74	26	71	0	.	26	72	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	2	100	19	93	100	42	25	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1409	11
	ash	0	.	0	.	0	.	0	.	6	100	75	77	105	65	17	61	55	78	22	100	33	.	0	.	0	.	0	.	2	.	0	.	43	88	0	.	0	.	54	55	15	77	7	100	50	57	38	69	9	100	143	67	7	100	0	.	0	.	0	.	16	71	0	.	1	100	0	.	29	71	0	.	7	85	61	70	87	59	28	78	0	.	61	82	4	100	0	.	0	.	0	.	18	100	61	88	0	.	0	.	51	76	0	.	0	.	5	100	0	.	17	100	11	72	2	100	50	51	10	90	19	94	24	77	0	.	4	78	0	.	37	100	9	85	13	73	68	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	65	23	58	54	69	27	100	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	1507	14
	oak	0	.	0	.	0	.	0	.	0	.	13	100	27	100	0	.	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	12	100	3	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	10	77	65	59	154	42	75	76	39	54	18	100	0	.	0	.	17	100	8	72	13	100	0	.	5	100	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	54	40	90	21	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	602	19
	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	.	154	61	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	136	66	56	83	117	74	114	45	489	33	191	59	108	64	242	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1677	17
	other broadleaves	0	.	0	.	0	.	0	.	0	.	1	100	250	53	5	100	0	.	17	55	0	.	0	.	0	.	0	.	13	.	0	.	3	100	0	.	0	.	1	100	40	62	36	50	104	45	96	36	42	62	30	51	1	100	0	.	0	.	0	.	1	100	2	100	7	100	0	.	41	76	0	.	0	0	0	.	25	56	8	73	0	.	27	58	2	100	0	.	0	.	9	86	1	100	24	92	0	.	0	.	0	.	0	.	0	.	59	78	63	56	30	93	120	52	146	57	324	32	19	61	55	47	72	81	0	.	7	78	12	84	35	98	4	100	12	87	3	100	0	.	0	.	82	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	112	56	106	37	98	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2145	12
	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
	total	0	.	0	.	0	.	0	.	218	71	1054	43	2995	20	1354	32	396	56	1469	26	60	.	170	65	145	73	0	.	52	.	0	.	411	44	0	.	0	.	1015	38	1208	32	1492	28	1231	27	3393	19	1653	23	1632	25	332	55	57	.	0	.	195	58	226	54	139	72	163	75	393	52	341	53	0	.	599	56	213	58	851	41	791	40	111	.	735	42	267	61	68	.	0	.	237	58	445	52	614	46	0	.	361	64	167	59	0	.	0	.	351	46	859	35	560	33	600	46	613	36	1261	22	629	32	336	43	400	37	164	100	223	64	1342	31	396	42	482	50	551	48	469	58	0	.	124	77	256	71	0	.	0	.	0	.	0	.	6	.	0	.	0	.	187	77	0	.	2605	18	2511	18	3206	19	190	72	0	.	0	.	35	.	19	.	0	.	0	.	0	.	0	.	45628	5
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	347	30	729	30	1244	28	1286	26	1713	26	2482	21	14694	8	7078	12	5129	13	2775	15	107	52	260	47	313	40	0	.	739	36	2561	21	3052	18	1787	22	19	100	3211	18	6262	13	3246	16	6692	12	8692	10	5161	12	2952	15	1509	23	1530	22	4004	17	359	40	1074	22	664	39	502	32	2065	22	912	28	1971	22	2160	19	1192	26	961	31	2554	19	1618	26	3393	19	1543	22	897	31	573	35	375	38	388	33	2167	22	2248	20	1536	25	1146	34	755	33	1038	28	655	31	1835	21	1508	26	7	100	117	88	0	.	348	38	44	66	384	33	618	30	1054	33	661	35	1102	33	1446	24	2430	22	603	33	129	44	1450	26	141	42	333	37	2245	17	1192	28	971	23	1042	25	206	51	600	41	86	58	577	37	3429	14	2930	18	3947	14	1015	26	1225	29	651	28	1069	24	1344	25	697	40	653	35	902	31	0	.	161313	2
	fir	524	37	192	58	328	39	1134	28	464	32	1353	29	2149	22	6918	12	4005	14	2808	20	85	82	305	55	304	40	0	.	181	60	248	45	1260	24	1001	32	0	.	526	37	302	41	705	29	300	45	710	30	10	100	3021	14	1332	24	2567	18	1628	23	411	42	1052	27	427	32	429	36	1423	26	235	45	162	42	485	28	855	25	256	42	477	35	531	35	1726	25	114	47	483	33	519	34	136	43	411	33	608	33	678	30	436	32	526	39	235	39	277	58	6	93	108	47	93	93	0	.	0	.	14	100	280	42	379	37	263	51	417	38	14	100	121	65	516	38	177	54	913	27	195	46	37	88	602	34	336	52	485	32	427	38	438	35	422	27	406	38	202	69	295	44	339	57	190	48	193	55	265	44	745	26	550	29	226	55	392	42	902	25	536	34	76	68	13	100	214	60	0	.	59037	3
	pine	144	51	62	56	106	43	220	45	2	100	10	100	340	54	43	57	364	28	303	46	162	61	101	72	196	37	0	.	13	87	102	79	0	.	179	58	62	79	0	.	219	39	440	29	56	71	716	29	973	21	311	30	9	100	98	55	60	68	85	75	17	100	0	.	0	.	14	100	0	.	126	75	0	.	107	66	0	.	183	54	0	.	55	69	244	33	84	50	127	40	2	297	135	43	34	70	4	100	6	100	67	49	91	54	236	40	0	.	58	71	49	75	0	.	0	.	78	100	0	.	0	.	43	83	40	100	29	100	15	100	22	100	0	.	0	.	0	.	15	71	5	100	19	100	23	77	0	.	47	58	0	.	40	58	0	.	0	.	51	100	0	.	795	26	291	30	420	33	0	.	34	71	169	39	67	54	285	33	136	43	268	43	89	66	0	.	9993	6
	larch	73	79	111	55	33	113	106	59	3	100	67	100	89	55	10	100	182	38	23	100	0	.	0	.	14	73	0	.	9	100	27	96	0	.	15	100	0	.	40	87	251	36	538	27	204	43	2032	14	5045	12	27	69	131	99	8	73	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	26	75	7	100	11	100	287	50	26	100	0	.	86	69	33	94	35	100	31	75	0	.	0	.	1	100	56	71	22	80	40	73	8	100	461	33	610	22	411	31	276	33	23	60	41	80	1260	18	390	29	395	44	0	.	0	.	0	.	73	45	32	72	0	.	44	97	138	76	0	.	14	71	0	.	0	.	18	72	0	.	118	60	0	.	6	100	0	.	23	74	4891	13	1954	16	1249	16	32	100	12	100	42	71	49	69	0	.	55	53	24	71	105	55	0	.	22474	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	39	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	73	7	100	1	100	157	39	1286	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	3	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	495	23	260	30	204	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2562	11
	other conifers	34	54	52	61	26	100	45	50	4	100	5	79	28	79	0	.	227	44	1	100	65	100	0	.	0	.	101	.	56	100	0	.	0	.	0	.	0	.	0	.	5	94	0	.	0	.	10	55	49	36	0	2109	37	98	122	88	0	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	185	96	-7	113	-8	119	6	71	-6	100	65	71	5	100	-5	252	13	72	0	.	0	.	0	.	2	100	7	100	0	.	46	95	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	1	100	0	.	0	.	7	69	0	.	5	100	8	100	0	.	58	100	86	89	11	100	5	71	0	.	0	.	0	.	118	87	1	100	63	99	0	.	0	.	5	87	3	100	4	75	0	.	8	100	9	136	25	62	26	74	0	.	0	.	0	.	0	.	1644	20
	beech	1782	18	1014	24	706	25	1883	23	272	34	693	27	2023	23	2330	14	3970	12	2738	14	415	44	781	29	1909	22	0	.	634	44	796	30	988	27	1411	25	0	.	806	25	507	38	385	33	45	77	556	30	0	.	3823	13	1153	25	715	28	1064	26	284	42	313	38	296	37	217	34	397	28	322	35	154	63	1154	29	657	29	473	40	683	26	1127	24	1315	21	1907	17	1464	22	1089	21	852	27	1550	20	620	28	227	60	654	29	466	31	299	36	835	25	0	.	122	44	0	.	711	27	705	30	471	30	1057	22	813	28	749	27	419	40	14	100	10	100	377	44	83	53	318	36	373	40	54	52	1039	28	210	54	107	35	307	30	343	31	1160	25	589	34	463	38	273	42	121	53	343	40	8	100	38	74	536	27	234	40	634	33	823	36	895	25	880	22	713	35	397	35	476	30	0	.	67620	3
	maple	284	26	108	35	160	30	108	37	73	75	125	55	694	19	335	21	371	26	431	21	92	50	173	38	168	36	0	.	54	58	69	47	240	31	138	45	9	78	351	27	65	49	88	95	23	70	87	39	5	100	572	19	110	30	98	39	214	28	72	55	89	45	90	62	58	79	32	40	111	29	35	41	282	40	150	34	64	42	206	37	212	34	338	29	78	43	165	43	156	27	55	41	186	35	207	33	74	34	82	47	335	41	110	42	67	55	35	83	56	55	11	100	46	54	65	65	24	47	39	45	23	68	0	1305	116	36	20	90	6	88	92	56	181	38	183	26	125	65	2	100	71	40	60	75	20	54	38	43	57	37	162	36	113	36	7	100	6	179	0	.	31	77	28	67	40	54	154	24	74	43	106	47	101	51	202	29	145	33	131	48	103	51	139	53	0	.	11312	4
	ash	449	23	321	34	215	36	143	46	78	70	194	44	128	42	400	34	591	25	209	48	216	49	63	50	173	38	0	.	138	54	218	56	255	36	536	27	16	100	190	41	47	58	3	248	87	47	80	43	97	100	845	19	413	62	218	34	114	36	41	64	7	100	7	87	1	100	22	91	248	60	48	65	44	63	408	32	178	41	97	40	234	38	203	41	151	36	75	40	43	49	151	47	166	43	190	42	114	61	170	62	302	33	379	34	256	43	43	61	66	100	50	61	45	47	114	42	194	42	173	36	20	74	135	61	79	66	24	72	12	100	137	52	85	48	71	49	82	47	51	59	33	46	192	50	1	100	5	100	16	74	113	51	240	46	38	57	172	55	0	.	300	54	60	46	54	64	192	36	173	48	342	40	149	38	300	31	291	29	84	66	144	40	166	38	0	.	14421	5
	oak	308	37	228	41	41	67	361	33	0	.	0	.	22	51	33	71	616	21	28	82	106	68	151	73	121	56	0	.	34	88	33	100	60	51	114	50	396	42	0	.	9	100	102	74	0	.	63	52	43	100	257	35	73	69	115	64	43	71	87	67	0	.	18	100	0	.	0	.	3	100	0	.	19	100	134	80	55	61	15	97	13	71	0	.	390	36	20	80	57	72	67	54	170	36	10	100	0	.	24	100	78	46	298	65	188	39	0	.	0	.	0	.	42	55	81	51	142	38	54	64	160	48	54	64	26	82	11	100	0	.	66	100	18	86	0	.	69	71	0	.	22	84	328	52	273	47	0	.	320	40	72	86	38	50	210	37	30	71	13	100	80	52	4	73	29	46	119	33	28	81	29	81	46	56	18	73	94	52	107	38	159	48	175	41	0	.	7922	7
	sweet chestnut	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	47	11	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	.	245	39	152	62	296	45	702	38	606	29	485	36	303	35	764	34	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	99	0	.	0	.	0	.	0	.	14	100	0	.	64	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3883	12
	other broadleaves	599	44	136	29	140	35	132	39	4	159	58	56	309	24	145	25	486	26	122	29	77	38	104	61	98	29	0	.	40	55	211	72	18	60	142	53	37	49	149	37	74	38	66	56	329	31	602	22	273	37	361	23	119	43	38	67	50	41	24	53	14	51	4	100	137	92	41	59	51	50	34	50	74	43	27	45	32	50	131	34	179	48	71	35	32	39	78	50	48	51	111	46	77	57	86	35	39	48	68	67	177	49	167	34	414	66	133	49	222	26	180	41	334	38	379	23	605	24	562	26	328	34	373	25	37	50	86	54	40	66	104	53	217	72	125	55	62	52	96	60	135	51	170	66	7	54	16	50	50	66	98	77	74	81	32	51	75	63	0	.	19	59	306	25	242	29	492	23	22	64	113	54	105	38	56	40	38	66	77	52	103	51	99	45	0	.	13244	5
	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	0	0	0	0	.	0	0	0	.	0	0
	total	4543	14	2952	17	3002	17	5418	14	2614	23	4987	19	20515	8	17293	8	15944	8	9437	11	1326	27	1937	24	3296	18	101	.	1899	25	4265	17	5874	15	5324	16	539	39	5273	15	7756	12	5579	13	7738	11	13864	8	12952	9	12169	9	4885	16	5511	15	7180	14	1363	25	2567	19	1510	28	1368	26	3994	19	1882	23	2530	20	4292	16	3721	19	2046	22	4625	16	3946	18	7096	15	4609	14	3302	18	2641	18	1794	22	3083	17	3922	17	3385	18	3034	20	3125	21	2375	23	3364	18	1337	22	3322	15	2457	19	1758	17	2186	19	2176	16	4287	12	2461	17	3159	16	1752	24	1251	30	891	31	2488	22	2247	21	4047	19	1554	26	580	38	3445	19	1481	29	1254	25	3039	16	2480	20	3000	17	2849	18	1159	29	1520	28	609	49	1563	27	10228	9	6106	12	8126	10	2127	22	2730	21	2487	20	3581	17	3638	17	2077	23	1866	24	2363	21	0	.	375425	1
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	347	30	729	30	1244	28	1286	26	1834	25	3084	20	16095	8	7480	11	5145	13	2995	14	107	52	281	44	324	39	0	.	771	34	2561	21	3188	18	1787	22	19	100	3677	17	7177	12	4229	14	7679	11	11402	9	6226	11	3237	15	1556	22	1530	22	4004	17	393	37	1137	21	679	38	529	31	2109	21	1057	27	1971	22	2539	18	1272	25	1280	27	2951	19	1618	26	3554	19	1646	21	897	31	573	35	375	38	402	32	2329	21	2248	20	1581	24	1183	33	755	33	1038	28	893	27	2017	19	1932	22	7	100	138	76	13	100	450	35	48	60	384	33	779	31	1259	30	1610	26	1167	31	1672	23	2613	20	700	31	129	44	1454	26	145	41	333	37	2245	17	1192	28	971	23	1043	25	206	51	600	41	151	51	577	37	4643	12	4370	14	5853	12	1028	26	1225	29	651	28	1075	24	1349	25	697	40	653	35	902	31	0	.	181288	2
	fir	524	37	192	58	328	39	1134	28	521	30	1425	27	2226	22	7446	11	4165	14	3253	18	85	82	305	55	304	40	0	.	181	60	248	45	1262	24	1001	32	0	.	635	34	302	41	822	27	300	45	739	29	10	100	3458	13	1353	24	2609	18	1628	23	415	42	1147	25	427	32	511	34	1727	23	235	45	162	42	653	33	866	25	364	37	503	33	575	33	1977	23	114	47	534	32	519	34	136	43	450	31	731	31	678	30	539	30	566	37	235	39	277	58	6	93	282	54	93	93	0	.	0	.	14	100	320	38	379	37	263	51	419	37	15	91	254	41	585	35	289	50	990	25	199	45	37	88	635	32	420	45	485	32	427	38	438	35	422	27	409	38	202	69	295	44	373	53	190	48	361	52	354	40	808	24	550	29	226	55	392	42	902	25	536	34	76	68	13	100	214	60	0	.	63668	3
	pine	144	51	62	56	106	43	220	45	22	90	10	100	408	47	43	57	364	28	303	46	162	61	101	72	196	37	0	.	13	87	102	79	0	.	179	58	62	79	0	.	334	36	547	26	56	71	771	28	1141	20	486	31	9	100	98	55	60	68	85	75	31	71	18	100	0	.	14	100	0	.	126	75	9	100	107	66	0	.	183	54	0	.	55	69	249	33	84	50	127	40	12	77	228	37	138	57	4	100	6	100	67	49	91	54	236	40	0	.	200	70	49	75	0	.	0	.	78	100	0	.	0	.	43	83	40	99	29	100	121	53	22	100	0	.	0	.	0	.	15	71	5	100	19	100	23	77	0	.	47	58	0	.	40	58	0	.	0	.	51	100	0	.	1127	22	411	23	545	28	0	.	34	71	169	39	75	50	285	33	136	43	268	43	89	66	0	.	11791	6
	larch	73	79	111	55	33	113	106	59	9	75	67	100	98	51	10	100	182	38	23	100	0	.	0	.	14	73	0	.	9	100	27	96	0	.	15	100	0	.	40	87	294	32	645	26	222	40	2283	13	5289	12	27	69	131	99	8	73	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	26	75	7	100	28	72	395	41	26	100	6	100	86	69	33	94	35	100	31	75	0	.	0	.	1	100	56	71	22	80	40	73	8	100	493	31	626	21	443	29	288	32	23	60	46	72	1378	17	390	29	395	44	0	.	0	.	0	.	98	42	32	72	51	91	44	97	138	76	0	.	14	71	0	.	0	.	18	72	0	.	118	60	0	.	6	100	0	.	23	74	5541	11	2559	14	1708	16	32	100	12	100	42	71	49	69	0	.	55	53	24	71	105	55	0	.	25291	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	35	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	73	7	100	1	100	207	34	1396	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	3	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	569	21	329	27	204	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2861	10
	other conifers	34	54	52	61	26	100	45	50	4	100	5	79	28	79	0	.	227	44	36	97	65	100	0	.	0	.	101	.	56	100	0	.	0	.	0	.	0	.	1	100	5	94	0	.	0	.	10	55	49	36	0	2109	45	82	122	88	0	100	0	.	0	.	4	100	0	.	0	.	6	100	0	.	2	100	185	96	-7	113	-8	119	7	65	-6	100	65	71	5	100	-5	252	13	72	0	.	1	100	0	.	2	100	7	100	0	.	46	95	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	1	100	0	.	0	.	11	57	0	.	5	100	8	100	0	.	58	100	86	89	11	100	5	71	0	.	0	.	0	.	118	87	1	100	63	99	0	.	0	.	5	87	3	100	4	75	3	100	8	100	9	136	25	62	26	74	0	.	0	.	0	.	0	.	1705	19
	beech	1782	18	1014	24	706	25	1883	23	279	34	953	30	2910	20	2728	14	4112	11	3379	12	443	42	920	27	2043	21	0	.	634	44	796	30	1181	24	1411	25	0	.	1093	22	562	35	495	28	53	68	562	30	0	.	4303	12	1347	23	715	28	1064	26	440	34	329	36	378	36	255	32	437	26	427	30	154	63	1185	28	675	28	779	34	862	23	1181	23	1471	20	2057	16	1476	21	1089	21	1065	25	1817	19	729	24	227	60	860	29	491	30	299	36	835	25	0	.	264	41	0	.	1040	30	971	26	649	26	1122	21	917	26	793	26	419	40	14	100	131	59	507	37	164	41	467	31	639	32	54	52	1082	27	294	46	107	35	307	30	343	31	1160	25	590	34	463	38	273	42	203	43	343	40	8	100	38	74	879	22	355	37	634	33	823	36	917	25	887	22	713	35	397	35	476	30	0	.	76329	2
	maple	284	26	108	35	160	30	108	37	73	75	156	47	869	16	338	21	375	25	519	19	92	50	182	36	168	36	0	.	60	54	69	47	273	28	138	45	9	78	447	24	89	45	113	75	76	56	89	38	6	89	649	18	163	29	113	36	214	28	72	55	110	38	111	53	64	72	37	37	125	27	35	41	284	40	192	31	53	66	251	33	224	32	412	25	81	42	170	42	156	27	61	38	199	33	236	31	74	34	90	44	348	39	110	42	67	55	35	83	56	55	11	100	47	53	65	65	73	37	48	41	30	57	0	1305	116	36	25	74	6	88	119	46	216	33	249	27	151	55	2	100	98	35	61	73	20	54	38	43	57	37	162	36	113	36	7	100	6	179	3	100	31	77	30	63	60	47	254	22	99	37	106	47	101	51	202	29	145	33	131	48	103	51	139	53	0	.	12721	4
	ash	449	23	321	34	215	36	143	46	85	65	269	38	233	37	417	32	646	24	230	44	250	44	63	50	173	38	0	.	140	53	218	56	298	34	536	27	16	100	244	34	61	48	9	101	136	37	118	37	106	92	988	19	420	61	218	34	114	36	41	64	23	58	7	87	3	71	22	91	277	54	48	65	51	55	469	29	265	34	125	35	234	38	264	37	154	35	75	40	43	49	151	47	184	40	251	38	114	61	170	62	353	30	379	34	256	43	48	56	66	100	67	52	57	40	117	41	244	35	183	34	39	59	159	53	79	66	27	63	12	100	174	46	93	45	84	43	151	52	51	59	33	46	193	50	1	100	5	100	16	74	113	51	240	46	38	57	172	55	0	.	300	54	81	38	78	48	246	32	201	43	342	40	149	38	300	31	298	29	84	66	144	40	166	38	0	.	15928	5
	oak	308	37	228	41	41	67	361	33	0	.	13	100	49	60	33	71	636	21	28	82	106	68	151	73	121	56	0	.	34	88	33	100	60	51	114	50	396	42	0	.	9	100	102	74	12	100	66	50	43	100	263	34	73	69	115	64	43	71	87	67	0	.	18	100	1	100	0	.	3	100	0	.	19	100	134	80	55	61	15	97	13	71	0	.	391	36	20	80	57	72	67	54	170	36	11	92	0	.	24	100	78	46	298	65	188	39	0	.	4	100	0	.	52	47	146	38	296	28	130	52	199	40	73	54	26	82	11	100	17	100	74	89	31	65	0	.	74	66	0	.	40	65	328	52	273	47	0	.	320	40	72	86	38	50	210	37	30	71	13	100	80	52	35	48	68	56	139	30	28	81	29	81	46	56	18	73	94	52	107	38	159	48	175	41	0	.	8524	7
	sweet chestnut	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	313	38	25	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	.	17	100	381	34	209	50	414	38	816	33	1095	21	677	31	411	31	1006	27	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	99	0	.	0	.	0	.	0	.	14	100	0	.	102	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5559	9
	other broadleaves	599	44	136	29	140	35	132	39	4	159	60	55	559	27	150	24	486	26	139	27	77	38	104	61	98	29	0	.	53	48	211	72	21	54	142	53	37	49	150	37	114	33	102	40	433	26	698	20	315	33	391	22	119	43	38	67	50	41	24	53	15	47	6	76	144	88	41	59	92	44	34	50	74	43	27	45	58	37	139	33	179	48	98	30	34	38	78	50	48	51	119	43	78	56	111	34	39	48	68	67	177	49	167	34	414	66	192	41	285	23	209	37	454	31	524	23	929	18	581	25	383	30	445	24	37	50	93	51	52	55	139	46	221	71	137	51	65	49	96	60	135	51	252	55	7	54	16	50	50	66	98	77	74	81	32	51	75	63	2	100	19	59	417	24	348	23	590	22	22	64	113	54	105	38	56	40	38	66	77	52	103	51	99	45	0	.	15389	5
	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	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2298536. Birmensdorf, Swiss Federal Research Institute WSL

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