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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NFI3
growing stock (stemwood)
protection forest against hillslope debris flows/landslides (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
protection forest against hillslope debris flows/landslides (2022)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
inside	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	97	99	537	49	1212	26	378	41	105	79	301	40	0	.	13	100	10	100	0	.	27	.	0	.	82	51	0	.	0	.	432	51	699	38	893	31	855	32	2456	23	1003	29	312	45	40	100	0	.	0	.	44	74	92	70	14	100	24	100	149	74	76	92	0	.	417	55	71	62	210	58	315	60	0	.	164	59	93	71	0	.	0	.	0	.	12	100	136	71	0	.	159	82	41	96	0	.	0	.	210	58	138	52	393	37	0	.	18	100	9	100	97	95	4	100	0	.	117	100	158	66	935	37	68	83	218	61	182	67	92	100	0	.	4	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	1194	27	1480	22	1851	22	11	100	0	.	0	.	32	.	5	.	0	.	0	.	0	.	0	.	18736	7
	fir	0	.	0	.	0	.	0	.	98	100	79	59	59	68	420	37	151	77	330	43	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	97	92	0	.	119	77	0	0	11	100	0	0	397	48	14	100	25	.	0	.	4	100	122	76	0	0	67	100	342	53	0	.	0	.	168	100	10	100	58	71	40	93	31	.	391	52	0	.	51	.	0	.	0	.	33	100	107	96	0	.	92	77	44	67	0	.	0	.	0	.	176	75	0	.	0	.	0	.	0	.	27	100	0	.	0	.	1	100	14	100	66	64	51	80	111	94	46	72	2	100	0	.	24	101	48	100	0	.	0	.	0	.	0	.	1	.	0	.	0	.	23	103	0	.	139	92	96	93	64	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4249	13
	pine	0	.	0	.	0	.	0	.	24	100	0	.	64	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	74	142	48	0	.	76	63	185	67	195	61	0	.	0	.	0	.	0	.	14	100	36	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	15	100	94	63	102	72	0	.	0	.	0	.	0	.	0	.	0	.	149	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	61	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	268	39	164	34	178	52	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	1937	16
	larch	0	.	0	.	0	.	0	.	7	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	62	83	65	11	80	214	42	250	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	106	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	78	14	61	31	62	12	100	0	.	11	100	114	55	0	0	0	.	0	.	0	.	0	.	16	100	0	.	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	684	26	582	29	481	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2750	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	76	109	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	60	65	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	302	31
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	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	.	45	68
	beech	0	.	0	.	0	.	0	.	5	100	299	70	916	46	494	51	285	63	526	31	25	.	176	72	134	72	0	.	0	.	0	.	163	52	0	.	0	.	277	51	32	71	97	47	9	100	6	100	0	.	675	34	148	64	19	.	0	.	113	59	34	62	121	100	23	100	44	60	86	71	0	.	52	67	33	69	369	58	138	57	29	.	126	57	95	74	20	.	0	.	165	62	200	55	67	53	0	.	227	65	28	62	0	.	0	.	0	.	113	64	0	.	479	87	228	54	138	49	60	71	84	69	22	82	0	.	0	.	86	70	197	47	131	83	184	62	219	54	0	.	31	80	79	94	0	.	0	.	0	.	0	.	1	.	0	.	0	.	78	71	0	.	0	.	0	.	241	36	80	88	0	.	0	.	92	.	7	.	0	.	7	.	0	.	0	.	8811	11
	maple	0	.	0	.	0	.	0	.	0	.	27	95	169	32	2	100	18	100	72	46	0	.	9	100	0	.	0	.	1	.	0	.	30	71	0	.	0	.	88	58	9	100	10	82	41	71	1	100	0	.	90	45	39	67	10	.	0	.	0	.	12	68	17	100	4	71	5	100	11	71	0	.	2	100	47	56	37	78	42	69	9	.	59	56	0	.	4	.	0	.	5	100	13	71	14	100	0	.	8	100	17	74	0	.	0	.	0	.	0	.	0	.	5	100	0	.	27	62	4	100	3	100	0	.	0	.	7	100	0	.	34	63	32	61	47	68	17	71	0	.	27	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	100	17	78	92	40	18	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1260	11
	ash	0	.	0	.	0	.	0	.	5	100	69	71	87	60	-1	100	82	62	19	100	35	.	0	.	0	.	0	.	0	.	0	.	31	93	0	.	0	.	65	57	11	61	7	100	60	74	56	72	12	100	220	65	41	86	0	.	0	.	0	.	19	72	0	.	1	100	0	.	20	71	0	.	13	100	101	61	81	55	7	100	0	.	34	86	0	.	0	.	0	.	9	100	12	76	43	82	0	.	0	.	85	93	0	.	0	.	-8	100	0	.	10	100	6	100	2	100	42	69	12	84	8	190	18	77	0	.	1	100	0	.	59	80	8	88	11	71	53	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	85	13	63	29	62	21	100	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	1530	16
	oak	0	.	0	.	0	.	0	.	0	.	13	100	27	100	0	.	23	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	11	100	1	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	72	91	65	146	40	71	79	38	54	11	93	0	.	0	.	18	100	45	93	6	100	0	.	11	100	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	55	38	88	20	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	656	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	62	12	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	.	22	100	152	57	47	85	107	73	100	47	463	35	211	62	70	62	312	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1681	17
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	0	260	59	7	72	15	100	19	48	0	.	2	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	30	55	26	57	69	52	104	42	31	63	32	51	1	100	0	.	0	.	0	.	2	100	1	100	3	100	0	.	78	90	0	.	1	100	0	.	45	65	4	100	0	.	53	75	2	100	0	.	0	.	8	81	1	100	20	80	0	.	0	.	0	.	0	.	0	.	75	66	49	54	11	110	124	51	123	50	282	32	28	52	28	58	78	74	0	.	5	89	11	86	45	77	6	83	7	83	2	100	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	68	59	90	38	120	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2023	13
	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
	total	0	.	0	.	0	.	0	.	236	72	1023	40	2811	22	1301	33	679	45	1297	25	60	.	200	68	144	72	0	.	28	.	0	.	312	45	0	.	0	.	960	38	929	33	1377	28	1056	28	3106	20	1602	23	1929	25	286	57	53	.	0	.	161	58	296	50	189	73	122	79	539	47	275	51	0	.	665	57	263	53	829	39	652	41	69	.	828	42	194	71	75	.	0	.	202	63	365	51	491	54	0	.	486	56	216	63	0	.	0	.	313	45	790	34	492	34	738	59	562	35	1118	22	623	34	235	45	440	36	118	100	186	60	1227	32	514	38	511	48	521	55	396	58	0	.	111	72	181	75	0	.	0	.	0	.	0	.	2	.	0	.	0	.	158	78	0	.	2473	19	2545	17	3118	18	130	75	0	.	0	.	138	.	25	.	0	.	7	.	0	.	0	.	43978	5
outside	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	464	31	585	29	1070	29	1442	26	1797	26	2571	21	14336	9	7709	11	6598	12	2884	16	75	52	245	49	494	47	0	.	999	31	2391	20	2573	19	1796	21	15	100	3044	22	5456	14	3097	16	5789	13	8680	10	4561	12	3380	17	1710	22	1962	20	4586	16	280	46	1111	22	701	35	548	30	1780	22	829	29	1899	23	1755	21	1678	23	846	31	2418	20	1463	26	3451	19	1493	23	1082	27	649	40	388	36	385	35	1880	22	2553	20	1381	26	1164	31	943	27	1092	26	545	31	1599	21	1226	27	0	.	98	85	0	0	232	43	85	68	407	40	522	31	982	35	598	38	1176	29	1241	26	2196	21	1162	36	103	49	1224	24	277	50	418	45	2038	16	854	27	1034	22	983	26	173	50	844	40	181	49	726	40	2992	15	2729	18	3839	14	1133	30	1137	29	860	29	1102	24	1292	23	915	32	654	37	1018	32	0	.	160670	2
	fir	512	36	185	48	350	51	1123	25	386	35	1299	26	2117	21	6833	11	4009	14	2660	19	61	86	234	59	310	41	0	.	268	49	216	51	1110	26	932	34	0	.	449	39	275	46	565	27	242	43	589	30	4	100	3021	15	1224	24	2316	19	1573	21	353	39	966	27	386	31	355	38	1235	25	191	49	156	41	370	28	770	26	224	43	482	33	491	35	1491	26	108	47	456	34	527	33	159	45	411	29	427	34	600	29	381	37	637	40	235	46	257	59	0	.	102	47	81	95	0	.	0	.	16	100	269	45	403	37	226	53	331	39	28	100	110	68	494	39	171	53	906	27	199	49	18	100	481	35	432	51	668	39	348	41	387	37	645	28	260	37	209	63	387	47	267	62	160	51	184	52	221	48	634	26	539	30	183	53	380	41	880	26	565	33	101	68	3	100	227	69	0	.	56046	3
	pine	212	43	260	52	88	44	231	47	2	100	8	100	355	48	51	52	426	26	289	42	180	67	136	56	222	41	0	.	15	75	126	67	0	.	203	56	64	81	0	.	230	37	482	29	34	78	710	30	961	21	353	30	8	100	112	55	50	62	91	75	42	100	0	.	0	.	15	100	0	.	84	72	3	100	116	65	0	.	169	64	3	100	73	60	230	33	87	55	122	38	5	100	180	40	51	80	4	100	6	100	98	50	87	55	250	41	0	.	35	71	49	76	0	.	0	.	81	100	0	.	0	.	46	91	13	100	18	100	18	100	7	100	0	.	0	.	0	.	9	88	5	100	89	91	27	80	0	.	42	54	0	.	80	60	0	.	0	.	16	100	0	.	718	24	310	29	382	33	0	.	58	72	212	35	76	62	406	36	171	40	288	43	118	49	0	.	10802	6
	larch	71	77	128	55	39	94	140	63	2	100	74	100	81	63	9	100	158	39	22	100	0	.	0	.	20	80	0	.	88	100	19	100	0	.	23	72	0	.	29	100	285	36	430	29	221	37	1921	15	4196	11	25	71	108	99	12	89	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	20	80	6	100	8	100	280	47	25	100	0	.	80	59	25	100	3	100	21	73	0	.	0	.	0	.	52	72	21	77	38	72	3	100	410	38	516	25	316	35	221	38	19	62	35	79	1127	19	385	29	302	51	0	.	0	.	13	100	138	48	28	73	0	.	38	100	87	84	0	.	9	78	0	.	0	.	7	90	0	.	102	54	0	.	62	100	0	.	3	76	4194	13	1815	17	1180	17	32	100	12	100	46	89	27	74	0	.	45	61	22	71	136	58	0	.	20035	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	7	100	0	.	120	48	1181	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	469	22	212	32	122	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2220	11
	other conifers	21	61	29	71	29	100	24	65	3	100	3	80	28	66	0	.	292	39	0	100	35	100	0	.	0	.	87	.	37	100	0	.	0	.	0	.	0	.	0	.	7	69	0	.	0	.	8	64	42	45	4	63	27	100	116	89	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	148	98	2	100	-8	127	3	100	18	100	65	71	4	100	7	77	10	77	0	.	2	100	0	.	2	100	5	100	0	.	31	87	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	100	0	.	0	.	2	100	0	.	4	100	0	.	0	.	17	100	53	88	4	100	3	71	0	.	0	.	0	.	78	99	0	.	44	100	0	.	0	.	4	100	8	100	2	61	0	.	6	100	33	71	14	76	9	73	0	.	0	.	0	.	0	.	1393	19
	beech	2043	17	970	22	772	26	1843	22	240	38	583	26	1949	23	2191	16	4274	12	2519	14	465	37	709	30	1900	22	0	.	464	46	737	31	874	29	1492	25	0	.	879	27	447	38	388	31	34	68	480	27	0	.	3300	13	964	25	903	31	1080	25	222	38	261	36	253	36	174	37	402	27	278	36	79	74	1032	28	659	30	538	40	774	26	1122	26	1348	25	1763	17	1662	22	1086	20	726	28	1537	20	529	27	211	69	571	29	415	36	247	35	938	24	0	.	137	42	0	.	684	27	610	31	346	34	997	23	709	27	676	27	312	42	12	100	9	100	358	42	75	58	263	36	382	48	43	65	816	26	247	44	130	44	267	30	428	29	1059	23	495	34	531	34	341	36	137	53	527	37	20	100	22	84	438	28	274	37	485	32	769	31	824	24	928	23	593	36	329	34	339	32	0	.	64938	3
	maple	248	25	135	33	113	39	101	34	101	44	104	47	605	20	278	21	355	27	429	21	105	44	202	37	119	37	0	.	51	58	60	49	157	31	96	43	7	81	313	29	57	49	10	62	30	58	99	33	2	100	521	18	76	34	70	41	237	35	65	50	77	44	80	61	68	88	24	38	102	29	43	61	146	39	173	34	67	40	164	38	175	34	422	30	66	45	137	51	106	35	78	43	272	31	146	34	77	44	35	44	270	40	76	44	76	56	39	85	44	58	6	100	24	96	55	51	4	45	14	352	25	86	6	88	93	43	37	62	0	.	72	58	135	43	186	26	110	48	0	.	84	44	81	75	21	67	34	42	53	31	132	31	116	48	2	100	8	89	0	.	60	56	16	74	25	50	118	26	93	44	106	43	97	34	205	31	151	28	103	48	76	57	82	56	0	.	10138	4
	ash	383	25	302	39	167	36	112	43	45	81	192	47	100	46	475	31	588	25	175	38	251	56	70	77	164	37	0	.	107	57	195	54	291	39	417	36	24	100	382	67	89	67	5	71	69	51	83	43	63	100	666	19	401	69	192	44	123	39	26	97	34	87	5	100	0	.	17	93	253	61	19	69	21	71	463	30	271	41	129	39	218	42	199	37	131	37	64	42	55	54	109	50	192	45	228	52	84	61	154	63	300	32	357	38	264	36	23	93	55	93	40	52	35	54	86	42	145	47	107	40	-2	973	84	61	65	79	43	66	10	100	100	47	77	50	45	49	114	48	6	76	42	65	164	47	0	.	4	100	18	76	121	51	123	51	39	53	164	60	0	.	226	54	77	60	43	64	140	40	209	49	277	41	251	35	210	30	243	34	71	80	174	41	164	46	0	.	13515	6
	oak	418	46	258	40	31	64	330	33	0	.	0	.	21	52	28	94	598	19	-1	223	128	56	55	97	52	54	0	.	41	65	34	100	57	57	127	46	423	41	0	.	9	100	107	78	0	.	147	69	34	100	234	34	92	62	129	79	65	59	87	62	0	.	18	100	0	.	0	.	2	100	0	.	11	100	171	94	52	60	7	100	16	60	7	100	322	37	10	100	52	75	70	55	171	40	9	100	0	.	37	100	107	47	297	54	169	38	0	.	0	.	0	.	32	53	66	53	154	42	44	89	142	51	41	73	27	85	42	100	0	.	56	100	17	87	0	.	74	76	0	.	25	86	390	48	226	48	0	.	356	39	83	90	48	45	244	38	47	61	17	100	294	66	3	74	37	59	97	36	20	100	13	100	53	50	27	71	118	42	104	37	159	49	109	49	0	.	8193	8
	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	.	151	43	12	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	.	227	47	140	69	236	42	703	35	574	27	428	42	225	37	678	31	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	90	0	.	6	100	0	.	0	.	15	100	0	.	55	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3503	12
	other broadleaves	551	47	134	37	126	36	95	48	17	57	37	72	339	22	155	28	432	27	156	38	122	47	87	65	86	29	0	.	30	52	130	71	12	42	98	50	32	58	229	59	135	59	29	62	212	33	605	21	216	39	350	24	102	47	33	58	90	45	13	58	15	55	1	100	11	59	55	58	34	56	31	52	58	47	13	72	51	48	139	36	198	38	70	51	64	53	62	46	44	50	169	45	129	45	84	36	40	38	63	59	178	47	124	41	116	44	146	43	205	27	181	36	244	38	289	25	537	25	463	28	282	39	358	24	36	50	90	54	20	68	106	57	217	68	54	43	33	74	63	59	138	49	127	69	4	61	10	44	39	53	41	59	68	85	82	55	77	69	0	.	28	73	351	26	159	34	425	25	22	58	99	51	107	41	60	38	13	72	78	48	125	65	47	55	0	.	11827	5
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	4924	15	2987	17	2785	18	5441	14	2594	23	4871	19	19952	8	17728	8	17730	8	9134	11	1421	26	1738	25	3368	19	87	.	2101	24	3909	17	5074	15	5184	16	565	39	5325	18	6998	12	5119	14	6630	12	13593	8	11271	8	11854	9	4711	17	5845	15	7803	13	1138	26	2504	19	1447	27	1179	27	3528	18	1689	23	2310	21	3464	17	4197	17	2076	22	4555	16	3713	18	7078	16	4322	14	3591	18	2651	19	1734	22	3278	17	3356	17	3570	18	2681	22	3195	20	2403	23	3196	18	1167	22	2920	16	2042	20	1477	18	1925	19	1892	17	3703	12	2254	17	2826	16	1399	24	1252	30	792	34	2506	21	1968	22	3649	18	2112	27	346	38	2868	18	1821	27	1498	26	2702	15	2184	20	3114	17	2403	18	1280	27	1979	29	619	45	2025	28	9044	10	5582	13	7432	10	2323	22	2375	22	2806	20	3425	17	3723	17	2182	22	1830	26	2241	24	0	.	363280	1
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	464	31	585	29	1070	29	1442	26	1894	25	3108	19	15549	9	8087	10	6704	12	3185	15	75	52	258	46	504	46	0	.	1027	30	2391	20	2654	18	1796	21	15	100	3476	20	6156	13	3990	14	6643	12	11136	9	5564	11	3692	16	1750	22	1962	20	4586	16	324	40	1203	21	715	34	572	29	1928	21	905	28	1899	23	2172	20	1749	23	1056	28	2733	19	1463	26	3616	18	1585	22	1082	27	649	40	388	36	398	34	2016	21	2553	20	1541	25	1205	30	943	27	1092	26	755	27	1737	20	1619	22	0	.	116	73	9	100	329	41	89	65	407	40	639	31	1141	31	1533	27	1244	28	1458	24	2378	20	1254	34	103	49	1227	24	277	50	418	45	2038	16	854	27	1034	22	983	26	173	50	844	40	230	44	726	40	4186	13	4209	14	5689	12	1144	29	1137	29	860	29	1134	23	1297	23	915	32	654	37	1018	32	0	.	179406	2
	fir	512	36	185	48	350	51	1123	25	484	34	1378	25	2175	21	7253	11	4160	14	2991	17	61	86	234	59	310	41	0	.	268	49	216	51	1112	26	932	34	0	.	546	36	275	46	683	26	242	43	600	29	4	100	3419	15	1238	24	2341	19	1573	21	357	39	1087	25	386	31	422	36	1577	23	191	49	156	41	538	37	780	26	282	37	522	31	521	34	1882	23	108	47	508	33	527	33	159	45	444	28	534	34	600	29	473	33	681	38	235	46	257	59	0	.	278	51	81	95	0	.	0	.	16	100	296	42	403	37	226	53	332	38	42	74	176	49	545	36	282	49	952	26	201	48	18	100	506	34	481	47	668	39	348	41	387	37	645	28	261	37	209	63	387	47	290	57	160	51	323	49	317	44	698	24	539	30	183	53	380	41	880	26	565	33	101	68	3	100	227	69	0	.	60295	3
	pine	212	43	260	52	88	44	231	47	26	93	8	100	419	43	51	52	426	26	289	42	180	67	136	56	222	41	0	.	15	75	126	67	0	.	203	56	64	81	0	.	323	34	624	25	34	78	786	28	1145	21	549	29	8	100	112	55	50	62	91	75	56	79	36	100	0	.	15	100	0	.	84	72	14	83	116	65	0	.	169	64	3	100	73	60	234	32	87	55	122	38	21	79	274	34	153	55	4	100	6	100	98	50	87	55	250	41	0	.	184	67	49	76	0	.	0	.	81	100	0	.	0	.	46	91	13	100	18	100	125	54	7	100	0	.	0	.	0	.	9	88	5	100	89	91	27	80	0	.	42	54	0	.	80	60	0	.	0	.	16	100	0	.	986	21	474	22	561	28	0	.	58	72	212	35	90	55	406	36	171	40	288	43	118	49	0	.	12739	6
	larch	71	77	128	55	39	94	140	63	9	79	74	100	92	57	9	100	158	39	22	100	0	.	0	.	20	80	0	.	88	100	19	100	0	.	23	72	0	.	29	100	340	32	513	27	232	35	2136	14	4446	11	25	71	108	99	12	89	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	20	80	6	100	20	72	386	39	25	100	0	.	80	59	25	100	3	100	21	73	0	.	0	.	0	.	52	72	21	77	38	72	3	100	424	37	529	25	346	32	234	36	19	62	46	65	1240	18	385	29	302	51	0	.	0	.	13	100	154	44	28	73	44	100	38	100	87	84	0	.	9	78	0	.	0	.	7	90	0	.	102	54	0	.	62	100	0	.	3	76	4878	12	2397	14	1661	17	32	100	12	100	46	89	27	74	0	.	45	61	22	71	136	58	0	.	22785	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	27	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	81	7	100	0	.	159	41	1290	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	552	21	277	29	122	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2522	11
	other conifers	21	61	29	71	29	100	24	65	3	100	3	80	28	66	0	.	292	39	30	97	35	100	0	.	0	.	87	.	37	100	0	.	0	.	0	.	0	.	1	100	7	69	0	.	0	.	8	64	42	45	4	63	31	88	116	89	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	2	100	148	98	2	100	-8	127	3	100	18	100	65	71	4	100	7	77	10	77	0	.	3	83	0	.	2	100	5	100	0	.	31	87	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	100	0	.	0	.	5	72	0	.	4	100	0	.	0	.	17	100	53	88	4	100	3	71	0	.	0	.	0	.	78	99	0	.	44	100	0	.	0	.	4	100	8	100	2	61	0	.	6	100	33	71	14	76	9	73	0	.	0	.	0	.	0	.	1437	19
	beech	2043	17	970	22	772	26	1843	22	245	37	882	29	2866	21	2685	16	4559	12	3045	12	490	35	886	28	2034	21	0	.	464	46	737	31	1037	26	1492	25	0	.	1156	24	479	36	485	27	42	58	485	27	0	.	3975	12	1112	24	922	30	1080	25	335	32	295	33	374	40	197	35	445	25	364	32	79	74	1084	27	692	29	907	33	912	24	1151	26	1474	23	1858	16	1682	22	1086	20	891	25	1737	18	596	25	211	69	798	27	444	34	247	35	938	24	0	.	250	37	0	.	1164	39	838	27	484	28	1057	22	793	25	699	27	312	42	12	100	95	65	555	32	206	57	447	33	600	36	43	65	847	25	326	40	130	44	267	30	428	29	1059	23	495	34	531	34	341	36	215	42	527	37	20	100	22	84	680	22	354	34	485	32	769	31	916	24	935	22	593	36	336	34	339	32	0	.	73749	2
	maple	248	25	135	33	113	39	101	34	101	44	131	42	774	17	280	21	373	26	501	19	105	44	211	35	119	37	0	.	52	57	60	49	187	29	96	43	7	81	401	26	66	45	20	51	71	47	100	33	2	100	611	17	115	32	80	38	237	35	65	50	89	39	97	54	72	83	29	36	113	27	43	61	148	38	220	29	104	38	205	33	184	32	481	27	66	45	141	50	106	35	83	41	286	29	160	32	77	44	42	40	287	38	76	44	76	56	39	85	44	58	6	100	29	81	55	51	32	54	18	277	29	77	6	88	93	43	44	54	0	.	106	44	167	37	233	25	128	42	0	.	111	38	81	75	21	67	34	42	53	31	132	31	116	48	2	100	8	89	6	100	60	56	16	72	42	43	211	23	110	38	106	43	97	34	205	31	151	28	103	48	76	57	82	56	0	.	11398	4
	ash	383	25	302	39	167	36	112	43	51	73	261	39	186	37	474	31	670	23	194	36	286	51	70	77	164	37	0	.	107	57	195	54	322	37	417	36	24	100	447	58	100	60	12	66	129	44	139	39	76	85	886	22	442	63	192	44	123	39	26	97	53	61	5	100	1	100	17	93	273	57	19	69	34	58	563	27	352	34	136	37	218	42	233	34	131	37	64	42	55	54	118	47	204	43	271	45	84	61	154	63	385	32	357	38	264	36	15	157	55	93	50	46	41	48	87	41	187	40	119	37	6	441	102	52	65	79	44	64	10	100	159	42	84	46	56	42	168	46	6	76	42	65	165	46	0	.	4	100	18	76	121	51	123	51	39	53	164	60	0	.	226	54	85	55	56	51	169	35	230	45	277	41	251	35	210	30	256	33	71	80	174	41	164	46	0	.	15045	5
	oak	418	46	258	40	31	64	330	33	0	.	13	100	48	61	28	94	621	18	-1	223	128	56	55	97	52	54	0	.	41	65	34	100	58	55	127	46	423	41	0	.	9	100	107	78	11	100	148	68	34	100	240	33	92	62	129	79	65	59	87	62	0	.	18	100	0	.	0	.	2	100	0	.	11	100	171	94	70	51	7	100	16	60	7	100	322	37	10	100	52	75	70	55	171	40	10	92	0	.	37	100	107	47	297	54	169	38	0	.	0	.	0	.	37	47	157	44	300	29	115	59	180	41	52	61	27	85	42	100	18	100	100	69	24	69	0	.	85	67	0	.	50	66	390	48	226	48	0	.	356	39	83	90	48	45	244	38	47	61	17	100	294	66	33	50	75	53	116	32	20	100	13	100	53	50	27	71	118	42	104	37	159	49	109	49	0	.	8849	7
	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	.	293	37	24	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	.	22	100	379	36	188	55	343	37	803	31	1036	21	639	35	295	32	990	24	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	90	0	.	6	100	0	.	0	.	15	100	0	.	97	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5184	9
	other broadleaves	551	47	134	37	126	36	95	48	17	57	37	72	599	29	162	27	446	26	175	34	122	47	89	64	86	29	0	.	30	52	130	71	15	39	98	50	32	58	229	59	166	49	55	43	280	28	709	19	248	35	382	23	102	46	33	58	90	45	13	58	16	50	2	73	14	51	55	58	112	65	31	52	59	46	13	72	96	40	143	35	198	38	122	43	66	52	62	46	44	50	177	43	130	45	105	33	40	38	63	59	178	47	124	41	116	44	221	36	254	24	193	35	368	31	412	23	818	19	491	26	310	36	436	24	36	50	95	51	31	53	151	46	224	66	61	39	35	70	63	59	138	49	179	57	4	61	10	44	39	53	41	59	68	85	82	55	77	69	3	100	28	73	419	24	249	26	545	22	22	58	99	51	107	41	60	38	13	72	78	48	125	65	47	55	0	.	13850	5
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	4924	15	2987	17	2785	18	5441	14	2830	22	5895	17	22763	8	19029	8	18408	8	10431	10	1481	25	1938	23	3512	18	87	.	2129	23	3909	17	5386	15	5184	16	565	39	6284	16	7927	11	6496	12	7686	11	16699	7	12873	8	13783	8	4997	17	5898	15	7803	13	1298	23	2800	18	1636	25	1301	26	4067	17	1964	21	2310	21	4129	17	4460	17	2905	19	5206	14	3782	18	7906	15	4516	14	3666	18	2651	19	1936	21	3643	16	3847	17	3570	18	3167	20	3411	19	2403	23	3196	18	1481	20	3710	14	2534	17	2215	23	2487	17	3010	13	4326	12	2490	16	3267	15	1517	24	1438	28	2019	23	3020	19	2479	20	4171	17	2508	25	346	38	2979	17	2001	25	1498	26	2702	15	2184	20	3114	17	2405	18	1280	27	1979	29	777	39	2025	28	11517	8	8126	10	10550	9	2452	22	2375	22	2806	20	3563	17	3749	17	2182	22	1837	26	2241	24	0	.	407258	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. 2297436. Birmensdorf, Swiss Federal Research Institute WSL

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