NFI Results

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 (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
protection forest (2022)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
inside	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	1402	40	919	42	9126	12	2965	20	238	49	571	33	0	.	43	91	107	100	0	.	273	88	1688	35	1015	37	1233	46	0	.	1327	34	4838	19	2313	28	3469	23	6876	15	3449	20	389	47	128	72	0	.	967	40	55	.	274	61	56	.	40	81	274	85	860	52	899	42	1573	34	352	39	308	55	2117	27	668	50	1372	36	82	100	0	.	98	.	35	100	0	0	700	49	1322	37	583	51	300	51	14	100	0	0	1086	39	3043	41	1265	34	0	.	82	71	30	100	368	60	10	100	361	49	627	49	1020	55	1191	40	555	85	1073	44	790	40	218	85	39	.	3	100	0	.	0	.	0	.	0	.	0	.	25	.	0	.	107	84	22	.	21	.	3506	20	3784	20	5598	17	368	57	159	70	263	56	337	49	418	68	0	.	0	.	0	.	0	.	81691	4
	fir	0	.	0	.	0	.	0	.	206	58	1019	44	1714	29	3435	19	825	44	1044	30	0	.	0	.	31	100	0	.	185	100	104	79	700	48	270	63	0	.	296	69	237	49	260	54	37	100	761	39	0	0	1085	32	254	60	0	.	253	82	0	.	126	53	65	.	143	100	373	58	337	61	37	100	733	49	560	53	94	64	509	43	576	59	836	33	0	.	0	.	11	.	0	.	53	100	691	57	551	43	394	39	121	63	19	100	0	.	18	100	208	52	0	.	0	.	0	.	34	100	354	59	276	60	242	73	482	47	0	.	307	56	272	47	538	56	950	44	57	100	0	.	45	71	166	.	0	.	0	.	0	.	0	.	16	.	0	.	11	100	70	.	96	.	162	93	474	52	872	36	132	65	0	.	70	100	318	50	150	51	0	.	0	.	0	.	0	.	25264	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	333	46	0	.	166	36	545	43	127	56	18	100	0	.	0	.	0	.	29	100	0	.	0	.	44	100	0	.	0	.	42	100	0	.	0	.	0	.	0	.	53	100	0	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	.	224	60	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	642	38	337	34	467	39	0	.	25	100	96	76	29	77	0	.	0	.	0	.	0	.	0	.	3669	13
	larch	0	.	0	.	0	.	0	.	0	.	0	.	37	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	148	51	219	50	40	88	1279	23	2815	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	123	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	100	14	100	0	.	0	.	300	60	445	40	351	51	221	62	0	.	66	92	1041	32	396	45	556	59	0	.	0	.	0	.	52	100	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3373	23	1988	23	1116	25	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14735	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	.	122	74	551	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	490	37	197	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1391	20
	other conifers	0	.	0	.	0	.	0	.	0	.	9	100	1	100	0	.	81	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	6	100	3	80	11	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	7	100	12	72	-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	1	100	0	.	0	.	0	.	7	100	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	.	294	42
	beech	0	.	0	.	0	.	0	.	157	78	202	48	2135	26	2253	24	722	47	1110	27	46	.	201	60	466	49	0	.	10	100	237	57	818	37	5	100	0	.	515	43	573	50	214	45	10	100	526	41	0	.	1578	24	464	44	0	.	693	45	47	.	33	59	13	.	10	100	57	52	353	50	23	100	672	38	227	41	834	48	991	32	415	50	1118	35	173	74	0	.	0	.	134	87	180	69	303	44	317	67	835	42	88	49	84	71	199	100	0	.	182	58	0	.	721	37	1235	32	718	35	1202	32	999	32	544	43	319	63	0	.	258	49	238	59	130	56	432	47	304	63	26	.	86	83	145	.	3	.	0	.	0	.	0	.	6	.	0	.	99	72	96	.	17	.	0	.	25	86	729	33	260	50	242	63	34	90	215	46	213	46	0	.	12	.	0	.	0	.	29535	6
	maple	0	.	0	.	0	.	0	.	8	100	80	58	673	21	178	32	37	100	306	42	0	.	57	100	47	100	0	.	0	.	69	58	212	42	40	100	0	.	335	43	122	46	56	70	51	100	119	43	0	.	158	52	73	58	0	.	121	44	0	.	43	60	38	.	16	71	17	73	103	44	35	78	135	80	92	65	155	74	400	35	210	51	495	34	0	.	0	.	0	.	24	74	91	92	113	54	77	59	64	78	170	91	12	100	-2	100	34	100	59	100	11	100	5	63	22	100	38	62	7	58	56	96	5	74	47	70	16	85	1	100	178	49	81	47	175	43	0	.	7	.	50	75	10	.	2	.	0	.	0	.	15	.	0	.	0	.	10	100	0	.	12	.	0	.	50	69	264	39	100	50	104	70	16	134	120	45	24	131	0	.	0	.	0	.	0	.	6548	8
	ash	0	.	0	.	0	.	0	.	0	.	178	72	123	70	272	70	87	91	60	63	16	.	0	0	10	100	0	.	0	.	218	100	174	59	34	91	0	.	225	49	17	100	8	73	103	85	90	66	0	.	266	58	85	67	0	.	41	69	0	.	1	100	0	.	0	.	0	.	84	61	0	.	112	79	115	64	141	68	67	60	23	100	258	45	0	.	0	.	0	.	42	91	29	100	260	56	11	72	242	69	106	69	0	.	0	0	0	.	211	100	2	100	24	63	27	77	82	64	123	64	25	95	107	47	26	64	50	72	0	.	74	98	34	80	66	61	291	71	81	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	18	100	0	.	31	.	69	57	40	95	192	54	78	75	167	83	11	100	50	71	13	100	0	.	0	.	0	.	0	.	5400	12
	oak	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	0	108	65	0	.	0	.	0	.	19	100	0	.	0	.	60	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	85	100	73	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	37	95	50	100	18	100	0	.	0	.	0	.	47	57	116	71	382	38	85	65	111	49	102	61	0	0	0	.	36	100	14	72	23	100	0	.	108	91	0	.	35	100	0	.	33	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	40	74	90	80	13	67	0	.	0	.	30	71	15	100	0	.	0	.	0	.	0	.	0	.	1813	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	365	55	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	356	46	294	67	334	53	270	44	957	35	468	52	254	57	1453	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4924	14
	other broadleaves	0	.	0	.	0	.	0	.	0	.	54	93	377	38	157	33	17	74	56	46	0	.	29	96	22	100	0	.	3	100	34	80	16	76	56	62	0	.	100	50	47	55	51	64	238	44	584	33	332	66	139	47	19	78	0	.	55	76	0	.	4	100	0	.	0	.	5	100	23	61	6	76	30	57	0	0	71	49	100	51	18	100	108	44	0	.	0	.	2	.	88	74	14	100	82	70	6	78	59	88	0	.	18	100	10	100	75	80	321	54	92	98	261	42	334	47	847	29	188	40	269	40	423	40	46	63	11	100	30	78	36	95	22	60	37	63	9	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	5	.	0	.	280	37	162	35	200	33	7	63	28	71	0	.	22	91	0	.	0	.	0	.	0	.	0	.	6735	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
	total	0	.	0	.	0	.	0	.	1774	38	2462	33	14311	11	9347	14	2115	31	3247	22	62	.	331	59	750	47	0	.	471	71	2410	30	2937	25	1639	40	0	.	2801	27	6079	17	3454	23	3949	21	10894	12	7823	14	3826	21	1041	36	0	.	2129	33	102	.	510	53	172	.	209	92	771	47	1759	38	1000	40	3306	28	1353	38	1703	35	4287	20	1917	39	4240	23	255	80	0	.	111	.	324	54	368	61	2168	34	2284	30	2238	31	848	47	197	71	227	88	1546	32	4872	31	2016	27	1613	24	2085	25	3154	19	3917	18	2397	23	3805	20	1547	35	1097	52	2053	32	1420	40	1902	35	2469	32	987	59	152	.	218	71	321	.	44	.	0	.	0	.	25	.	48	.	0	.	245	69	194	.	179	.	8595	14	7151	15	9517	14	985	41	725	49	523	50	1152	36	828	45	0	.	12	.	0	.	0	.	181997	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	879	39	1162	50	1177	41	4999	21	2599	22	4360	17	2115	21	45	81	249	66	307	60	0	.	502	52	641	50	1279	37	1031	39	4	75	1825	34	2338	29	1348	30	2837	28	2671	25	2524	23	1850	27	1036	34	1925	24	2600	24	262	72	1221	31	460	50	507	43	1334	33	713	51	669	48	791	48	849	42	1090	44	420	84	1274	44	1342	40	1306	30	831	46	196	44	236	61	320	55	1006	37	859	44	649	45	677	46	755	36	826	38	13	100	223	71	200	77	0	.	0	.	0	.	0	.	84	72	0	.	278	56	558	59	415	53	419	43	517	38	384	55	700	44	98	76	1131	32	138	68	253	51	1892	23	805	40	741	42	706	42	135	77	192	56	10	100	404	71	839	41	428	51	315	51	331	65	593	43	431	48	1015	39	1440	32	84	94	439	50	578	38	0	.	79396	4
	fir	249	72	40	96	144	60	965	34	402	54	532	61	505	41	3098	22	4019	18	1516	26	32	64	136	60	342	54	0	.	162	72	263	76	245	70	330	62	4	100	205	64	145	67	169	67	253	69	241	100	0	.	2805	25	1253	35	3202	20	1311	27	522	53	1041	39	490	38	454	46	1379	39	31	81	156	75	333	60	459	51	0	.	0	.	191	55	409	49	184	59	514	34	269	65	140	46	318	40	72	89	139	58	185	84	416	73	317	41	506	58	0	.	0	.	167	100	0	.	0	.	0	.	0	.	0	.	0	.	66	67	0	.	6	100	207	59	18	100	176	100	144	61	75	100	993	36	34	74	325	50	380	52	472	47	619	32	442	49	259	69	487	54	70	100	104	92	0	.	46	72	67	85	137	60	140	61	196	63	709	41	272	45	94	85	19	100	86	58	0	.	37903	5
	pine	26	100	21	71	21	100	74	100	0	.	0	.	260	89	22	100	449	35	300	70	45	85	66	69	138	50	0	.	21	100	45	71	0	.	265	67	67	100	0	.	123	58	243	51	52	100	473	50	608	37	256	49	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	57	100	8	100	0	.	72	78	117	77	202	66	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	216	51	71	66	157	65	0	.	0	.	140	58	63	80	192	42	153	49	86	73	56	100	0	.	6564	11
	larch	30	94	55	64	106	71	95	81	0	.	0	.	121	79	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	200	53	81	65	196	66	502	36	2135	22	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	0	.	1	100	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	29	100	27	100	0	.	79	71	119	67	61	100	0	.	20	100	196	58	63	71	0	.	0	.	0	.	0	.	185	51	0	.	0	.	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	1325	35	588	39	159	62	0	.	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	7379	11
	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	.	102	73	706	29	0	.	0	.	0	.	0	.	0	.	0	.	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	.	126	45	17	78	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1002	23
	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	.	0	.	7	100	0	.	0	.	0	.	19	53	0	.	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	337	100	0	.	0	.	0	.	0	.	66	100	11	100	6	100	40	77	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	1870	29
	beech	1223	27	818	30	688	34	1839	30	165	59	292	52	263	63	1041	26	3565	14	2302	20	263	52	684	41	657	37	0	.	283	65	512	50	248	46	1253	36	0	.	420	44	102	57	377	45	14	76	31	100	0	.	1837	24	969	34	852	37	662	40	235	46	137	49	280	36	207	47	283	47	133	83	30	73	640	51	566	53	99	58	85	100	548	40	373	60	1986	22	1444	31	794	30	774	37	1104	31	127	52	0	.	243	60	502	36	267	46	594	30	0	.	0	.	0	.	96	83	91	62	25	100	0	.	26	89	0	.	424	56	0	.	0	.	383	64	1	100	0	.	310	72	45	100	1043	30	20	95	78	51	335	37	355	40	946	32	539	45	592	45	135	68	160	72	193	82	0	.	0	.	132	99	54	78	256	44	481	44	664	41	668	35	746	45	340	43	432	32	0	.	42386	4
	maple	366	30	182	36	53	82	34	74	17	100	186	54	97	68	287	42	361	24	396	27	140	49	60	44	123	56	0	.	60	74	13	86	121	54	51	63	0	.	35	56	40	75	7	100	35	71	0	.	3	100	350	28	125	39	51	51	180	45	100	57	138	48	45	82	142	88	27	59	33	126	0	.	29	71	82	83	86	71	0	.	33	66	192	55	101	53	168	45	174	40	76	45	195	41	59	63	0	.	23	71	144	50	42	100	108	51	0	.	0	.	0	.	0	.	0	0	1	100	2	100	0	.	5	.	106	58	0	.	0	.	7	108	87	67	0	.	37	66	2	100	108	50	0	.	29	66	32	59	79	37	65	64	109	47	38	71	0	.	0	.	60	75	9	100	0	.	53	80	0	.	108	53	48	59	209	39	87	50	243	55	70	60	248	51	0	.	7212	7
	ash	258	45	380	44	32	83	74	87	0	.	76	70	-21	150	179	44	558	33	26	70	121	92	34	56	127	64	0	.	166	70	192	87	309	56	251	42	9	100	35	96	42	100	2	100	24	100	14	100	0	.	503	32	335	81	99	51	16	77	70	61	16	100	4	100	3	100	6	100	428	70	0	.	4	100	470	54	130	67	30	100	115	94	0	.	231	46	138	48	56	58	134	61	63	67	62	72	0	.	0	.	173	39	262	58	238	45	18	100	0	.	0	.	0	.	7	100	123	84	0	.	0	.	0	.	135	86	0	.	0	.	107	52	52	71	0	.	29	100	16	59	24	74	0	.	9	77	12	100	32	90	79	87	208	58	70	64	29	100	0	.	39	100	5	97	23	80	13	74	37	100	263	62	38	83	144	49	213	40	173	65	71	59	119	66	0	.	8560	9
	oak	349	55	80	70	0	.	260	53	0	.	0	.	12	100	68	73	434	24	0	.	78	100	4	100	88	66	0	.	9	100	0	.	51	80	109	61	384	58	0	.	0	.	64	100	0	.	0	.	0	.	260	52	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	0	.	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	4	100	92	72	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	9	100	129	71	48	66	0	.	403	50	25	100	10	71	231	49	0	.	41	100	38	100	7	100	21	71	60	65	0	.	17	100	20	100	0	.	98	85	176	41	110	55	92	68	0	.	5652	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	103	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	0	56	92	128	67	233	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	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	.	533	38
	other broadleaves	271	45	174	36	140	44	98	67	0	.	21	60	86	43	56	47	547	28	54	41	97	52	25	58	25	62	0	.	20	100	197	63	44	56	121	66	8	86	55	94	36	75	3	100	133	76	14	170	125	92	254	45	107	52	63	60	48	47	0	.	26	55	7	100	-2	100	18	71	57	74	0	.	7	76	3	100	0	.	31	88	118	79	17	84	39	48	94	69	4	100	136	63	2	100	69	70	13	91	0	.	254	55	122	47	65	61	17	71	120	100	15	79	14	100	137	55	379	56	52	74	0	.	0	.	20	68	0	.	1	100	16	67	290	98	1	100	31	60	194	84	58	56	3	75	4	75	24	53	116	71	22	57	118	86	46	66	122	75	0	.	13	100	57	60	62	52	145	48	0	.	144	67	161	64	42	62	21	59	42	44	127	63	114	59	0	.	6635	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	3166	20	2245	22	2121	26	4404	20	1747	46	2288	36	6322	19	7350	17	14824	11	6710	16	822	36	1258	37	1834	32	252	.	1223	40	1920	36	2299	27	3450	24	475	55	2625	28	3039	25	2293	25	3545	25	4152	21	6120	16	8137	16	3992	23	6895	17	4860	20	1291	35	2578	27	1329	34	1352	36	3048	26	1405	38	1031	46	1823	35	2849	32	1404	45	567	67	2342	32	2333	35	4488	19	3312	23	1606	29	1589	31	2281	27	1452	31	1020	40	1100	43	2309	29	2413	28	2853	23	47	71	423	55	575	51	227	52	367	47	872	40	250	55	172	60	5	.	1080	47	558	59	423	53	1323	37	981	41	562	60	1251	39	586	51	3402	22	347	45	766	36	2675	22	2286	27	2498	25	2219	27	1370	33	993	37	350	75	920	51	2590	27	1257	35	1148	31	558	55	1551	35	1537	33	2867	28	2991	24	1733	29	1288	30	1903	26	0	.	205091	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	879	39	2564	31	2096	30	14125	11	5563	14	4598	17	2685	18	45	81	292	58	414	51	0	.	775	46	2330	29	2295	26	2264	30	4	75	3152	24	7176	16	3660	21	6306	18	9547	13	5973	15	2239	23	1164	31	1925	24	3567	20	317	62	1495	27	516	46	547	40	1608	31	1573	37	1568	31	2364	28	1201	32	1398	36	2536	26	1942	34	2714	27	1388	28	831	46	294	44	271	54	320	55	1706	29	2182	28	1232	34	977	36	769	35	826	38	1098	39	3265	38	1466	31	0	.	82	71	30	100	368	60	94	65	361	49	905	38	1578	41	1607	32	974	52	1590	32	1174	32	918	39	137	62	1134	32	138	68	253	51	1892	23	805	40	741	42	732	40	135	77	300	47	32	75	425	68	4345	18	4213	18	5913	16	699	43	752	37	694	37	1352	32	1858	29	84	94	439	50	578	38	0	.	161087	3
	fir	249	72	40	96	144	60	965	34	609	41	1551	36	2219	24	6533	14	4844	17	2559	19	32	64	136	60	373	50	0	.	347	63	366	59	946	40	600	44	4	100	501	48	383	40	429	42	291	61	1003	38	0	0	3889	20	1507	31	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	509	43	768	47	1244	27	184	59	514	34	280	62	140	46	372	37	762	52	690	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	354	59	276	60	242	73	549	42	0	.	313	55	479	37	556	55	1126	41	202	52	75	100	1037	35	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	521	48	938	34	269	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	63168	4
	pine	26	100	21	71	21	100	74	100	0	.	0	.	367	67	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	201	46	576	34	52	100	639	38	1154	28	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	0	.	0	.	53	100	330	39	100	63	97	53	4	71	119	51	57	100	8	100	0	.	85	68	117	77	202	66	0	.	46	74	66	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	224	60	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	858	31	408	30	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10232	8
	larch	30	94	55	64	106	71	95	81	0	.	0	.	158	64	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	348	37	300	41	237	57	1781	20	4950	14	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	123	71	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	300	60	524	35	469	42	282	53	0	.	86	75	1237	28	459	40	556	59	0	.	0	.	0	.	237	46	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	4698	19	2576	20	1275	23	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	22114	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	225	52	1256	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	617	31	214	39	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2393	15
	other conifers	42	75	0	.	71	100	85	72	0	.	14	74	1	100	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	7	100	0	.	0	.	6	100	22	47	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	4	100	3	64	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2163	26
	beech	1223	27	818	30	688	34	1839	30	323	48	494	36	2398	25	3294	18	4287	14	3412	16	309	47	885	34	1123	30	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	591	33	25	61	557	39	0	.	3416	17	1433	27	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1077	30	964	31	1491	30	2159	21	1444	31	794	30	907	34	1285	28	430	35	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	816	34	1326	30	743	34	1202	32	1025	32	544	43	743	42	0	.	258	49	621	45	132	56	432	47	614	48	71	73	1128	28	166	88	81	49	335	37	355	40	946	32	546	45	592	45	234	50	256	59	210	76	0	.	25	86	861	32	314	43	498	38	515	42	879	33	881	28	746	45	352	42	432	32	0	.	71921	3
	maple	366	30	182	36	53	82	34	74	25	75	266	42	771	21	465	29	399	24	702	23	140	49	117	54	170	49	0	.	60	74	82	51	334	33	91	56	0	.	370	39	162	39	63	63	86	66	119	43	3	100	508	24	198	32	51	51	301	32	100	57	181	39	83	64	158	79	45	46	136	46	35	78	164	67	174	52	241	54	400	35	244	45	687	29	101	53	168	45	174	40	99	39	286	40	172	41	77	59	87	60	314	54	54	81	106	52	34	100	59	100	11	100	5	63	22	100	39	61	9	50	56	96	9	62	153	46	16	85	1	100	184	48	168	41	175	43	37	66	9	78	158	42	10	100	31	63	32	59	79	37	80	55	109	47	38	71	10	100	0	.	71	65	9	100	50	69	317	35	100	50	212	43	64	55	329	29	111	48	243	55	70	60	248	51	0	.	13760	5
	ash	258	45	380	44	32	83	74	87	0	.	254	55	102	90	451	46	645	31	87	49	137	82	34	56	138	59	0	.	166	70	409	67	484	42	284	38	9	100	260	44	60	77	10	61	127	72	104	59	0	.	770	29	419	66	99	51	57	54	70	61	17	94	4	100	3	100	6	100	511	59	0	.	116	76	584	45	270	48	97	52	138	80	258	45	231	46	138	48	56	58	176	51	92	55	323	48	11	72	242	69	279	36	262	58	238	45	18	100	211	100	2	100	24	63	35	64	205	56	123	64	25	95	107	47	161	73	50	72	0	.	181	51	86	53	66	61	320	65	97	84	24	74	0	.	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	75	54	63	67	205	50	115	60	430	50	48	68	194	40	226	38	173	65	71	59	119	66	0	.	13959	7
	oak	349	55	80	70	0	.	260	53	0	.	0	.	17	74	68	73	542	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	0	0	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	0	.	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	0	.	0	.	47	57	119	69	474	33	85	65	111	49	102	61	50	100	0	.	36	100	14	72	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	111	66	73	54	0	.	17	100	50	58	15	100	98	85	176	41	110	55	92	68	0	.	7465	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	358	45	294	67	390	47	397	37	1190	31	468	52	254	57	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5456	12
	other broadleaves	271	45	174	36	140	44	98	67	0	.	75	69	463	32	213	27	564	27	110	31	97	52	54	57	48	57	0	.	23	87	231	55	61	46	177	49	8	86	155	46	83	45	54	60	371	39	598	33	457	54	393	34	126	45	63	60	103	46	0	.	30	50	7	100	-2	100	24	60	81	55	6	76	38	48	3	100	71	49	131	44	136	70	125	40	39	48	94	69	5	75	224	48	16	87	151	50	19	68	59	88	254	55	140	43	76	55	92	67	441	48	106	85	275	40	471	37	1225	26	239	35	269	40	423	40	66	48	11	100	31	75	53	69	312	91	38	61	40	50	194	84	58	56	3	75	4	75	24	53	116	71	22	57	118	86	46	66	122	75	5	100	13	100	337	33	224	29	345	28	7	63	172	57	161	64	64	51	21	59	42	44	127	63	114	59	0	.	13369	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	3166	20	2245	22	2121	26	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2278237. Birmensdorf, Swiss Federal Research Institute WSL

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