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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NFI5
growing stock (stemwood)
protection forest against channel processes (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
protection forest against channel processes (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	.	1250	43	510	48	9094	13	2774	20	211	53	319	42	0	.	4	.	107	100	0	.	273	88	1842	33	990	38	1233	46	0	.	891	44	4810	19	1646	32	3568	23	5686	16	3148	21	148	53	132	70	0	.	1138	37	0	.	230	71	56	.	0	.	243	95	468	56	899	42	1637	33	187	50	375	49	1877	28	668	50	1427	36	0	.	0	.	98	.	35	100	0	0	794	45	1349	36	583	51	300	51	14	100	0	0	893	43	3017	41	1144	36	54	100	82	71	30	100	417	54	128	92	593	38	627	49	1020	55	984	44	555	85	1122	42	767	42	37	100	39	.	3	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	107	84	0	.	21	.	3019	22	3197	19	5065	18	368	57	159	70	263	56	323	51	406	70	0	.	0	.	0	.	0	.	75480	4
	fir	0	.	0	.	0	.	0	.	206	58	862	50	1483	31	3243	19	798	45	614	38	0	.	0	.	38	83	0	.	185	100	104	79	696	48	270	63	0	.	99	98	237	49	143	54	37	100	571	46	0	0	636	37	296	54	0	.	253	82	0	.	86	71	65	.	0	.	0	.	309	66	37	100	733	49	537	55	94	64	661	44	576	59	693	33	0	.	0	.	11	.	0	.	53	100	805	50	551	43	253	44	121	63	19	100	0	.	18	100	208	52	0	.	0	.	0	.	34	100	407	53	276	60	250	70	482	47	0	.	307	56	270	47	526	57	816	50	57	100	0	.	20	.	0	.	0	.	0	.	0	.	0	.	16	.	0	.	11	100	0	.	96	.	162	93	493	50	709	40	132	65	0	.	70	100	312	51	150	51	0	.	0	.	0	.	0	.	22197	7
	pine	0	.	0	.	0	.	0	.	0	.	0	.	106	80	87	71	0	.	100	73	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	76	236	58	0	.	98	46	230	56	57	100	18	100	0	.	0	.	0	.	29	100	0	.	0	.	44	100	0	.	0	.	42	100	0	.	0	.	58	60	0	.	53	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	46	74	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	104	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	452	39	321	35	406	43	0	.	25	100	96	76	20	100	0	.	0	.	0	.	0	.	0	.	2780	14
	larch	0	.	0	.	0	.	0	.	0	.	0	.	37	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	53	203	54	-9	100	1559	21	2851	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	152	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	100	14	100	0	.	0	.	428	51	445	40	434	48	366	45	0	.	66	92	1312	27	421	43	563	59	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3127	19	1691	22	1340	30	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15355	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	121	75	510	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	507	36	315	37	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1503	19
	other conifers	0	.	0	.	0	.	0	.	0	.	9	100	44	79	0	.	81	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	4	65	11	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	-19	104	7	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	1	100	0	.	0	.	4	72	46	73	9	81	0	.	0	.	0	.	0	.	314	41
	beech	0	.	0	.	0	.	0	.	141	86	123	62	1612	31	2158	24	542	55	798	33	0	.	63	.	369	52	0	.	10	100	237	57	493	47	5	100	0	.	450	48	546	53	115	53	0	.	518	42	0	.	1245	28	385	48	0	.	693	45	0	.	33	59	13	.	0	.	11	100	121	53	23	100	638	40	181	47	651	59	844	34	415	50	1118	35	0	.	0	.	0	.	18	71	180	69	456	39	317	67	454	43	88	49	84	71	199	100	0	.	182	58	0	.	755	35	1235	32	718	35	1253	30	1094	30	548	43	319	63	0	.	229	50	101	90	149	58	411	48	150	76	26	.	69	.	0	.	3	.	0	.	0	.	0	.	6	.	0	.	99	72	0	.	17	.	0	.	30	74	662	36	260	50	242	63	34	90	186	51	190	50	0	.	12	.	0	.	0	.	25326	6
	maple	0	.	0	.	0	.	0	.	8	100	16	100	559	23	187	31	0	.	280	44	0	.	57	.	47	100	0	.	0	.	69	58	156	51	40	100	0	.	256	53	90	51	109	83	51	100	98	50	0	.	90	75	61	61	0	.	121	44	0	.	28	75	38	.	0	.	6	100	85	49	35	78	199	64	84	70	167	69	404	35	210	51	472	35	0	.	0	.	0	.	24	74	91	92	82	56	96	52	49	97	170	91	12	100	-2	100	34	100	101	72	11	100	5	63	22	100	32	72	7	58	90	72	5	74	47	70	16	85	0	.	134	62	41	61	148	47	0	.	7	.	16	.	0	.	2	.	0	.	0	.	15	100	0	.	0	.	10	100	0	.	12	.	0	.	48	71	229	35	100	50	104	70	16	134	120	45	24	131	0	.	0	.	0	.	0	.	5939	9
	ash	0	.	0	.	0	.	0	.	0	.	108	100	198	58	272	70	10	204	48	75	0	.	0	.	10	100	0	.	0	.	218	100	94	68	34	91	0	.	165	61	0	.	3	100	87	100	84	70	0	.	174	76	85	67	0	.	41	69	0	.	0	.	0	.	0	.	0	.	66	74	0	.	112	79	13	100	94	89	113	44	23	100	258	45	0	.	0	.	0	.	42	91	29	100	194	65	46	79	242	69	106	69	0	.	0	0	0	.	211	100	49	96	37	54	27	77	58	80	123	64	1	100	71	50	26	64	50	72	0	.	74	98	31	87	66	61	156	100	81	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	18	100	0	.	31	.	36	60	37	100	111	59	78	75	167	83	11	100	50	71	0	.	0	.	0	.	0	.	0	.	4581	13
	oak	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	0	58	100	0	.	0	.	0	.	19	100	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	85	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	37	95	50	100	18	100	0	.	5	100	0	.	54	51	116	71	155	46	64	81	97	55	102	61	0	0	0	.	36	100	0	.	0	.	0	.	108	91	0	.	0	.	0	.	33	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	40	74	15	58	12	74	0	.	0	.	30	71	15	100	0	.	0	.	0	.	0	.	0	.	1329	18
	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	.	209	61	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	278	53	129	84	334	53	270	44	552	46	468	52	225	64	1250	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	55	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3874	16
	other broadleaves	0	.	0	.	0	.	0	.	0	.	51	100	316	33	157	33	6	100	28	56	0	.	27	.	22	100	0	.	3	100	34	80	11	100	56	62	0	.	64	65	37	64	13	77	157	52	480	38	359	62	69	46	17	85	0	.	55	76	0	.	4	100	0	.	0	.	5	100	25	61	6	76	34	54	0	.	50	56	131	42	18	100	121	41	0	.	0	.	2	.	74	85	14	100	30	59	7	65	59	88	0	.	18	100	10	100	78	77	273	60	114	80	273	40	370	44	588	30	224	35	202	47	389	42	46	63	11	100	23	95	0	0	12	71	47	54	9	89	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	0	0	.	0	.	244	41	272	58	140	34	7	63	28	71	0	.	22	91	0	.	0	.	0	.	0	.	0	.	5969	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	0	.	0	.	0	.	0	.	1605	40	1679	41	13466	11	8879	14	1705	36	2189	27	0	.	152	.	660	50	0	.	471	71	2563	29	2440	28	1639	40	0	.	1925	31	5984	17	2467	26	3890	22	9465	13	7229	15	2438	25	994	38	0	.	2301	31	0	.	409	64	172	.	0	.	310	79	1073	48	1000	40	3395	27	1008	46	1520	38	4223	20	1917	39	4143	24	0	.	0	.	111	.	195	60	368	61	2380	32	2367	29	1703	34	848	47	197	71	227	88	1484	31	4766	31	1881	28	1880	22	2122	25	2233	22	4357	17	2533	22	3783	20	1547	35	1097	52	1683	35	1186	46	1882	36	2254	34	517	65	152	.	107	.	0	.	44	.	0	.	0	.	50	70	48	.	0	.	245	69	0	.	179	.	7619	14	6424	15	8749	14	985	41	725	49	523	50	1094	37	779	47	0	.	12	.	0	.	0	.	164649	3
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	351	37	495	37	866	42	904	38	1546	42	1586	36	7302	16	2854	21	4458	17	2439	19	45	81	288	59	324	57	0	.	502	52	848	45	1757	31	1225	37	4	75	2733	27	3144	24	2470	24	3889	22	4929	18	3097	20	2091	25	1093	32	1925	24	2741	23	317	62	1327	29	460	50	547	40	1475	31	1253	42	724	45	914	43	1014	37	1393	41	843	51	1274	44	1782	32	1388	28	904	43	196	44	236	61	359	50	1031	36	1021	39	741	40	677	46	755	36	826	38	253	79	309	54	377	54	0	.	0	.	0	.	36	100	84	72	19	100	305	52	730	48	792	39	660	38	789	33	684	40	881	40	98	76	1219	31	138	68	253	51	1994	22	855	38	782	40	706	42	166	65	192	56	32	75	404	71	1512	30	1200	41	1467	31	671	48	594	43	431	48	1059	38	1452	32	84	94	439	50	578	38	0	.	99610	3
	fir	249	72	40	96	144	60	965	34	402	54	689	51	798	37	3316	21	4024	18	2183	22	32	64	136	60	342	54	0	.	162	72	263	76	250	69	330	62	4	100	456	50	145	67	286	57	253	69	432	64	0	.	3254	23	1282	35	3202	20	1311	27	522	53	1081	37	490	38	596	42	1751	33	60	57	156	75	333	60	482	49	0	.	56	75	191	55	951	34	184	59	514	34	269	65	140	46	439	39	88	74	141	58	325	58	416	73	317	41	506	58	0	.	0	.	167	100	0	.	0	.	0	.	0	.	0	.	1	100	66	67	26	100	6	100	209	58	29	72	314	67	144	61	75	100	1034	35	200	84	325	50	380	52	472	47	619	32	442	49	259	69	487	54	140	71	104	92	0	.	48	70	230	62	266	57	140	61	196	63	715	40	272	45	94	85	19	100	86	58	0	.	42526	5
	pine	26	100	21	71	21	100	74	100	0	.	0	.	286	81	22	100	449	35	300	70	45	85	66	69	138	50	0	.	21	100	45	71	0	.	265	67	67	100	0	.	186	51	340	42	98	71	540	45	969	31	326	40	0	.	229	58	0	.	50	100	0	.	0	.	0	.	0	.	4	100	176	100	0	.	41	100	0	.	0	.	0	.	0	.	330	39	100	63	97	53	2	100	119	51	69	83	8	100	0	.	72	78	117	77	202	66	0	.	0	.	89	79	0	.	4	100	0	.	0	.	0	.	0	.	0	.	40	100	120	97	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	407	49	115	48	219	52	0	.	0	.	140	58	72	72	192	42	153	49	86	73	56	100	0	.	7797	10
	larch	30	94	55	64	106	71	95	81	0	.	0	.	144	68	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	278	42	347	48	290	49	1038	26	2678	18	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	0	.	72	99	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	29	100	27	100	61	77	100	60	157	54	70	88	0	.	20	100	343	43	131	53	31	75	0	.	0	.	0	.	189	50	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	2436	31	998	36	302	46	0	.	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	11054	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	95	0	.	9	100	141	57	957	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	145	41	19	71	76	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1372	19
	other conifers	42	75	0	.	71	100	85	72	0	.	5	100	0	.	0	.	441	48	2	100	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	8	93	5	100	0	.	3	100	22	48	0	.	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	337	100	5	100	0	0	0	.	0	.	66	100	11	100	6	100	40	77	0	.	5	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	0	.	2	77	0	.	0	.	14	88	1	100	0	.	0	.	0	.	0	.	0	.	1910	29
	beech	1223	27	818	30	688	34	1875	30	181	54	371	44	1265	41	1152	26	3756	14	2775	18	309	47	822	36	962	32	0	.	283	65	512	50	574	39	1253	36	0	.	485	39	129	50	496	38	25	61	201	63	0	.	2170	21	1166	30	852	37	662	40	282	42	137	49	280	36	218	45	330	41	365	54	30	73	675	48	612	49	281	47	358	54	548	40	715	42	2159	21	1740	31	794	30	889	34	1192	29	178	43	0	.	624	52	502	36	267	46	594	30	0	.	0	.	0	.	135	64	91	62	25	100	6	100	26	89	0	.	424	56	32	100	58	100	542	49	52	97	34	100	464	58	45	100	1059	29	166	88	78	51	335	37	355	40	946	32	539	45	592	45	135	68	256	59	193	82	0	.	0	.	229	61	54	78	256	44	481	44	699	39	691	34	746	45	340	43	432	32	0	.	49293	4
	maple	366	30	182	36	53	82	34	74	17	100	250	44	339	37	287	42	399	24	431	25	140	49	60	44	180	46	0	.	60	74	13	86	188	42	51	63	3	100	175	33	72	61	43	81	37	68	20	78	3	100	418	25	149	36	51	51	180	45	100	57	187	40	45	82	158	79	38	51	69	72	0	.	35	62	91	77	86	71	0	.	33	66	259	43	101	53	168	45	174	40	76	45	198	43	114	50	13	100	38	58	144	50	42	100	111	50	1	100	0	.	0	.	40	76	0	0	7	87	15	54	2	100	27	85	106	58	46	97	3	77	51	59	164	46	27	100	37	66	2	100	142	45	10	100	29	66	38	52	79	37	65	64	109	47	38	71	0	.	0	.	60	75	11	88	8	83	187	47	10	100	108	53	48	59	232	36	87	50	243	55	70	60	248	51	0	.	8828	6
	ash	258	45	380	44	32	83	74	87	0	.	146	60	-11	304	179	44	635	31	40	55	137	82	34	56	162	54	0	.	166	70	192	87	389	49	251	42	9	100	97	59	60	77	7	75	95	51	20	75	0	.	596	30	335	81	99	51	16	77	70	61	17	94	4	100	3	100	6	100	446	67	0	.	4	100	571	46	177	56	30	100	115	94	105	71	231	46	138	48	56	58	134	61	106	57	139	64	0	.	0	.	173	39	262	58	238	45	18	100	0	.	0	.	9	100	7	100	147	72	7	100	24	100	40	90	135	86	8	100	0	.	107	52	112	61	0	.	164	84	16	59	24	74	1	100	9	77	12	100	32	90	79	87	208	58	70	64	29	100	0	.	39	100	78	66	98	77	125	67	37	100	273	60	38	83	151	47	226	38	173	65	71	59	119	66	0	.	10105	8
	oak	349	55	80	70	0	.	391	48	0	.	0	.	27	71	68	73	544	22	0	.	78	100	4	100	88	66	0	.	9	100	0	.	52	79	109	61	384	58	0	.	0	.	64	100	0	.	0	.	0	.	324	46	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	6	100	0	.	17	71	0	.	210	47	13	100	11	100	31	86	158	52	0	.	0	.	0	.	71	64	502	81	285	51	0	.	0	.	0	.	0	0	8	71	319	45	21	100	40	70	0	.	50	100	0	.	0	.	50	74	23	100	0	.	0	.	2	100	44	82	129	71	48	66	0	.	403	50	25	100	10	71	231	49	0	.	41	100	38	100	7	100	100	73	102	45	12	100	17	100	20	100	26	100	98	85	176	41	110	55	92	68	0	.	6543	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	260	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	.	0	.	0	.	137	56	166	100	56	92	150	59	638	42	0	.	32	74	204	68	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1731	23
	other broadleaves	271	45	174	36	140	44	98	67	0	.	25	53	330	38	56	47	578	27	82	36	97	52	27	56	35	52	0	.	20	100	197	63	70	47	129	62	14	65	91	65	107	50	172	59	264	47	164	43	156	75	324	40	126	45	63	60	52	44	0	.	26	55	7	100	-2	100	18	71	66	66	0	.	11	61	3	100	22	100	37	75	118	79	36	56	39	48	94	69	4	100	150	58	41	89	125	58	56	60	40	100	254	55	122	47	186	65	41	54	175	75	15	79	73	61	191	44	652	42	294	61	132	56	176	57	20	68	132	61	13	64	53	69	300	94	48	95	31	60	194	84	147	64	178	98	4	75	24	53	116	71	22	57	118	86	46	66	122	75	5	100	13	100	140	38	149	33	263	33	0	.	144	67	161	64	52	54	21	59	42	44	131	61	114	59	0	.	9867	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	3166	20	2245	22	2121	26	4596	20	2146	40	3071	30	10480	15	7935	16	15373	10	8251	14	884	34	1437	33	2258	28	252	.	1223	40	2127	34	3280	23	3652	23	485	54	4089	23	4149	20	4230	20	4962	20	7748	15	7882	14	9525	14	4319	21	6895	17	5005	20	1393	33	2774	25	1329	34	1562	34	3619	24	2266	31	1086	44	1997	33	3194	29	1969	37	1397	39	2342	32	3848	25	4744	18	3681	23	1606	29	1718	30	2612	25	1750	28	1240	35	1768	35	2309	29	2413	28	2977	22	373	60	721	37	978	38	384	40	451	40	1807	27	723	38	471	38	498	41	1107	46	1014	42	999	39	1861	29	1486	33	1127	41	1721	39	586	51	3706	21	845	47	766	36	2784	21	2336	27	2539	25	2219	27	1401	32	993	37	544	60	920	51	4741	21	2736	27	3280	20	1051	45	1562	35	1537	33	3028	27	3039	24	1733	29	1292	30	1903	26	0	.	250636	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	351	37	495	37	866	42	904	38	2795	30	2096	30	16396	10	5628	14	4669	16	2758	17	45	81	292	58	431	49	0	.	775	46	2690	26	2747	24	2458	29	4	75	3623	23	7954	15	4116	19	7457	16	10614	12	6245	14	2239	23	1225	30	1925	24	3879	20	317	62	1557	26	516	46	547	40	1718	29	1721	34	1623	30	2551	26	1201	32	1768	34	2719	25	1942	34	3209	24	1388	28	904	43	294	44	271	54	359	50	1825	28	2371	26	1324	32	977	36	769	35	826	38	1146	37	3326	38	1521	30	54	100	82	71	30	100	454	50	212	63	612	37	932	37	1750	38	1777	30	1215	44	1911	28	1451	29	918	39	137	62	1222	31	138	68	253	51	1994	22	855	38	782	40	732	40	166	65	300	47	32	75	425	68	4531	18	4397	18	6532	15	1039	37	754	37	694	37	1382	31	1858	29	84	94	439	50	578	38	0	.	175091	2
	fir	249	72	40	96	144	60	965	34	609	41	1551	36	2280	24	6560	14	4822	17	2797	19	32	64	136	60	380	49	0	.	347	63	366	59	946	40	600	44	4	100	555	44	383	40	429	42	291	61	1003	38	0	0	3889	20	1578	30	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	717	41	768	47	1643	24	184	59	514	34	280	62	140	46	493	36	893	45	692	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	407	53	276	60	251	70	549	42	26	100	313	55	479	37	556	55	1130	40	202	52	75	100	1054	34	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	541	46	938	34	398	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	64723	4
	pine	26	100	21	71	21	100	74	100	0	.	0	.	393	63	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	264	42	576	34	98	71	639	38	1199	27	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	58	60	0	.	53	100	330	39	100	63	97	53	4	71	119	51	69	83	8	100	0	.	85	68	117	77	202	66	0	.	46	74	89	79	0	.	4	100	0	.	0	.	0	.	12	100	0	.	40	100	224	60	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	858	31	436	29	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10577	8
	larch	30	94	55	64	106	71	95	81	0	.	0	.	181	57	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	445	33	551	36	281	50	2597	16	5530	13	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	224	59	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	489	46	545	34	591	38	436	41	0	.	86	75	1655	23	551	35	594	56	0	.	0	.	0	.	242	45	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	5564	17	2689	19	1642	26	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	26409	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	262	46	1467	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	653	30	334	36	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2875	13
	other conifers	42	75	0	.	71	100	85	72	0	.	14	74	44	79	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	8	93	5	100	0	.	8	75	26	42	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	5	100	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	4	100	3	64	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2224	25
	beech	1223	27	818	30	688	34	1875	30	323	48	494	36	2877	25	3310	18	4298	14	3573	16	309	47	885	34	1331	27	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	611	32	25	61	719	35	0	.	3416	17	1551	26	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1202	28	964	31	1834	27	2159	21	1740	31	794	30	907	34	1372	27	634	31	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	891	31	1326	30	743	34	1260	30	1120	30	548	43	743	42	32	100	287	45	643	44	201	50	445	45	614	48	71	73	1128	28	166	88	81	49	335	37	355	40	946	32	546	45	592	45	234	50	256	59	210	76	0	.	30	74	890	31	314	43	498	38	515	42	886	33	881	28	746	45	352	42	432	32	0	.	74620	3
	maple	366	30	182	36	53	82	34	74	25	75	266	42	898	20	474	28	399	24	711	23	140	49	117	54	227	42	0	.	60	74	82	51	344	32	91	56	3	100	431	34	162	39	151	64	88	65	119	43	3	100	508	24	210	31	51	51	301	32	100	57	214	36	83	64	158	79	45	46	153	42	35	78	234	56	174	52	253	51	404	35	244	45	731	27	101	53	168	45	174	40	99	39	289	41	196	37	109	47	87	60	314	54	54	81	108	51	36	96	101	72	11	100	46	67	22	100	39	61	22	42	92	70	32	73	153	46	62	75	3	77	184	48	206	38	175	43	37	66	9	78	158	42	10	100	31	63	38	52	79	37	80	55	109	47	38	71	10	100	0	.	71	65	11	88	56	62	416	29	110	46	212	43	64	55	351	28	111	48	243	55	70	60	248	51	0	.	14767	5
	ash	258	45	380	44	32	83	74	87	0	.	254	55	187	64	451	46	645	31	89	48	137	82	34	56	172	51	0	.	166	70	409	67	484	42	284	38	9	100	262	44	60	77	10	61	182	54	104	59	0	.	770	29	419	66	99	51	57	54	70	61	17	94	4	100	3	100	6	100	511	59	0	.	116	76	584	45	270	48	143	41	138	80	363	38	231	46	138	48	56	58	176	51	135	49	333	46	46	79	242	69	279	36	262	58	238	45	18	100	211	100	49	96	46	48	35	64	205	56	130	61	25	95	111	45	161	73	57	64	0	.	181	51	144	51	66	61	320	65	97	84	24	74	1	100	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	113	49	136	62	236	45	115	60	440	49	48	68	201	39	226	38	173	65	71	59	119	66	0	.	14686	7
	oak	349	55	80	70	0	.	391	48	0	.	0	.	33	61	68	73	602	22	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	36	100	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	2	100	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	5	100	0	.	54	51	124	67	474	33	85	65	137	44	102	61	50	100	0	.	36	100	50	74	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	115	64	113	41	12	100	17	100	50	58	42	73	98	85	176	41	110	55	92	68	0	.	7872	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	415	40	294	67	390	47	419	35	1190	31	468	52	257	56	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	134	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5605	12
	other broadleaves	271	45	174	36	140	44	98	67	0	.	75	69	646	25	213	27	583	26	110	31	97	52	54	57	57	50	0	.	23	87	231	55	81	42	186	47	14	65	155	46	144	40	184	55	421	35	644	31	515	49	393	34	144	41	63	60	107	45	0	.	30	50	7	100	-2	100	24	60	91	51	6	76	45	43	3	100	71	49	169	37	136	70	157	34	39	48	94	69	5	75	224	48	55	71	155	48	63	54	99	66	254	55	140	43	197	61	119	54	448	47	129	71	346	34	561	33	1240	26	518	37	333	36	565	34	66	48	143	57	37	65	53	69	312	91	95	55	40	50	194	84	147	64	178	98	4	75	24	53	116	71	47	60	118	86	46	66	122	75	5	100	13	100	385	29	421	39	404	24	7	63	172	57	161	64	74	46	21	59	42	44	131	61	114	59	0	.	15836	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23946	9	16814	10	17079	10	10439	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5719	17	5291	20	485	54	6015	18	10133	13	6697	16	8852	15	17213	9	15112	10	11963	12	5313	19	6895	17	7305	16	1393	33	3184	23	1501	32	1562	34	3928	22	3339	26	2086	29	5392	21	4202	25	3489	26	5619	18	4259	25	7990	17	4744	18	3681	23	1717	28	1913	27	2980	23	4130	22	3607	23	3470	24	3157	24	2610	26	3204	21	1857	28	5487	28	2859	22	2264	20	2573	21	4041	17	5080	15	3004	19	4280	19	2654	28	2111	34	2682	26	3047	25	3368	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12360	12	9160	13	12028	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	415285	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2280224. Birmensdorf, Swiss Federal Research Institute WSL

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