Result query system

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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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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. “protective forest [2022]”) – is assigned to all measurement cycles.

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
growing stock (stemwood)
protection forest against hillslope debris flows/landslides (2022) · main tree species
regional demarcation: forest district (2023)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		forest district (2023)
		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	.	203	.	531	62	1891	31	268	78	27	100	368	42	0	.	39	100	0	.	0	.	0	.	0	.	124	82	0	.	0	.	534	56	552	53	1015	44	948	38	2719	26	1035	41	63	69	0	.	0	.	0	.	56	.	176	78	0	.	41	81	142	80	388	95	0	.	579	69	166	63	268	63	362	56	0	.	286	66	84	100	0	.	0	.	0	.	0	.	246	64	0	.	0	.	57	94	0	.	0	.	543	56	191	72	378	53	0	.	37	100	31	100	226	89	10	100	0	.	255	.	276	.	970	44	133	92	141	74	109	100	184	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	22	.	0	.	1248	42	1939	26	2240	29	0	.	0	.	0	.	15	.	12	.	0	.	0	.	0	.	0	.	22157	8
	fir	0	.	0	.	0	.	0	.	0	.	190	71	163	59	431	59	236	89	632	41	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	200	91	0	.	119	100	0	.	105	100	0	0	388	61	4	100	0	.	0	.	0	.	90	63	0	.	144	100	376	58	7	100	0	.	317	100	24	100	13	100	52	78	75	.	310	63	0	.	0	.	0	.	0	.	112	100	296	84	0	.	141	75	18	100	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	196	67	144	70	265	96	26	100	0	.	0	.	45	71	167	.	0	.	0	.	0	.	0	.	16	.	0	.	0	.	72	.	0	.	8	100	160	89	153	88	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	0	.	5793	15
	pine	0	.	0	.	0	.	0	.	0	.	0	.	84	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	61	46	100	62	59	264	73	94	67	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	62	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	447	52	173	39	84	69	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	1754	22
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	80	213	70	40	82	170	66	232	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	20	73	44	74	24	100	0	.	5	100	264	71	0	0	0	.	0	.	0	.	0	.	52	100	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	819	30	798	40	348	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3264	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	101	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	84	51	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	262	48
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	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	.	8	100	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	7	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	51
	beech	0	.	0	.	0	.	0	.	0	.	80	79	805	40	296	80	253	69	628	34	46	.	139	75	135	.	0	.	0	.	0	.	360	55	0	.	0	.	206	66	42	71	148	55	10	100	8	100	0	.	342	45	200	73	0	.	0	.	48	.	22	75	0	.	10	100	47	60	127	100	0	.	45	76	46	72	420	70	281	63	55	.	131	83	174	74	0	.	0	.	117	.	78	96	277	52	0	.	384	76	38	71	0	.	0	.	0	.	31	95	0	.	41	83	463	59	39	61	0	.	82	100	4	100	0	.	0	.	244	52	252	56	139	63	13	100	259	72	0	.	87	83	148	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	99	.	0	.	0	.	0	.	456	45	81	.	0	.	0	.	29	.	23	.	0	.	0	.	0	.	0	.	8493	11
	maple	0	.	0	.	0	.	0	.	0	.	64	68	181	38	0	.	38	100	167	45	0	.	0	0	0	.	0	.	0	.	0	.	61	69	0	.	0	.	157	77	32	100	36	94	51	100	5	100	0	.	84	62	24	100	0	.	0	.	0	.	43	60	0	.	16	71	11	100	18	100	0	.	5	100	10	128	133	86	68	83	23	.	158	50	0	.	0	.	0	.	0	.	8	100	65	68	0	.	14	100	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	.	16	73	0	.	2	100	0	.	0	.	0	.	1	100	46	64	40	73	0	.	0	.	0	.	50	75	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	158	55	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1825	15
	ash	0	.	0	.	0	.	0	.	0	.	70	100	84	95	0	.	77	100	33	100	17	.	0	.	0	.	0	.	0	.	0	.	86	93	0	.	0	.	40	100	17	100	5	100	106	85	0	.	0	.	210	73	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	4	100	102	71	141	68	21	100	0	.	80	81	0	.	0	.	0	.	0	.	43	100	77	100	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	22	72	0	.	24	100	0	.	24	100	46	80	0	.	5	.	0	.	0	.	3	100	0	.	139	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	2	100	89	90	52	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	0	.	1710	20
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	50	78	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	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	27	77	26	65	258	52	36	72	45	64	0	.	0	.	0	.	36	100	50	74	23	100	0	.	11	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	74	84	85	15	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	748	24
	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	.	157	100	24	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	.	32	100	127	66	22	100	166	74	136	67	602	47	240	89	34	75	341	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1947	23
	other broadleaves	0	.	0	.	0	.	0	.	0	.	4	100	235	56	0	.	11	100	31	70	0	.	1	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	2	100	30	74	137	69	113	60	78	49	57	86	34	73	2	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	7	70	0	0	36	66	12	84	0	.	41	65	0	.	0	.	0	.	13	.	37	100	56	97	0	.	0	.	0	.	0	.	0	.	22	78	53	97	2	100	142	63	174	81	382	49	20	100	86	68	198	65	0	.	0	.	29	81	36	95	10	100	10	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	72	57	100	43	117	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2413	15
	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
	total	0	.	0	.	0	.	0	.	203	.	939	49	3455	24	995	50	693	56	1860	30	62	.	180	68	135	.	0	.	0	.	0	.	642	52	0	.	0	.	1140	45	732	46	1846	35	1315	32	3305	24	1714	31	1214	37	229	74	0	.	0	.	104	.	365	63	0	.	211	92	576	51	540	75	0	.	1007	66	348	63	1054	47	867	50	156	.	1005	45	258	80	0	.	0	.	131	.	277	76	1018	53	0	.	539	72	151	71	0	.	0	.	645	51	540	42	446	53	424	43	835	44	1358	32	785	45	284	57	589	45	264	.	281	.	1696	37	713	40	622	64	177	88	600	82	0	.	220	71	325	.	0	.	0	.	0	.	0	.	48	.	0	.	0	.	199	.	0	.	2773	25	3310	22	3724	23	159	.	0	.	0	.	58	.	49	.	0	.	0	.	0	.	0	.	50390	6
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	354	37	507	36	881	42	914	38	2624	31	1600	34	14716	11	5435	15	4699	16	2446	19	45	81	253	65	439	49	0	.	793	46	2729	26	2646	25	2492	29	4	75	3142	25	7502	16	3160	22	6602	18	8046	13	5296	15	2202	24	1241	30	1959	24	3926	20	266	72	1395	28	523	46	511	43	1595	31	1355	34	1652	31	1993	27	1049	36	1525	38	2397	27	1963	34	2966	25	1330	30	917	43	301	44	274	54	363	50	1604	31	2401	26	1336	32	933	37	777	35	833	38	620	51	3198	40	1162	36	54	100	47	100	0	.	236	47	205	66	616	37	684	34	1503	41	824	39	1100	48	1790	30	1362	31	750	42	140	62	1233	31	142	68	255	51	2026	22	863	38	797	40	718	42	169	66	302	47	10	100	433	68	3332	19	2514	25	4396	18	1056	37	761	37	699	37	1387	31	1869	29	85	94	445	50	585	38	0	.	155276	3
	fir	252	72	43	91	147	60	975	34	621	41	1384	39	2141	25	6212	15	4647	17	2205	21	32	64	137	60	383	49	0	.	353	63	369	59	964	40	609	44	4	100	362	48	387	40	313	44	294	61	910	40	0	0	3559	21	1597	30	3232	20	1590	26	531	53	1086	37	560	35	460	46	1414	39	366	57	201	62	760	35	1007	38	81	73	675	43	705	51	1353	26	185	59	520	34	283	63	143	46	385	36	602	54	698	36	444	43	534	59	338	39	510	58	18	100	130	59	169	100	0	.	0	.	35	100	414	52	278	60	255	70	550	43	26	100	121	95	339	43	299	58	1127	41	205	52	76	100	1017	35	34	74	331	50	382	52	482	47	630	32	445	49	260	69	502	53	70	100	202	68	156	98	386	54	800	36	400	44	141	61	268	53	1031	32	426	34	95	85	19	100	87	58	0	.	59771	4
	pine	28	100	21	71	21	100	75	100	0	.	0	.	312	75	110	60	455	35	403	55	45	85	68	70	186	45	0	.	22	100	45	71	0	.	268	67	69	100	0	.	266	42	408	42	52	100	581	42	954	28	295	45	18	100	230	58	0	.	50	100	0	.	0	.	0	.	44	100	4	100	178	100	0	.	41	100	0	.	58	60	0	.	54	100	334	39	101	63	99	53	4	72	120	51	72	84	8	100	0	.	85	68	118	77	203	66	0	.	17	100	89	79	0	.	4	100	0	.	0	.	0	.	14	100	0	.	41	100	9	100	0	.	0	.	0	.	0	.	30	71	10	100	0	.	20	100	0	.	23	71	0	.	30	100	0	.	0	.	69	100	0	.	426	35	266	41	551	37	0	.	25	100	237	46	84	65	193	42	156	48	87	73	57	100	0	.	8939	9
	larch	31	91	55	64	106	71	95	81	0	.	0	.	181	57	0	.	91	71	0	.	0	.	0	.	27	71	0	.	0	.	58	100	0	.	40	87	0	.	51	100	393	36	346	40	246	56	2453	17	5362	14	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	0	.	155	73	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	58	100	14	100	30	100	27	100	448	49	528	35	553	41	420	43	0	.	82	80	1409	24	557	35	602	56	0	.	0	.	0	.	190	50	0	.	0	.	0	.	67	100	0	.	23	100	0	.	0	.	0	.	0	.	57	73	0	.	22	100	0	.	68	100	4804	19	1929	22	1312	30	40	100	30	100	9	100	20	89	0	.	23	71	26	100	179	54	0	.	23441	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	261	46	1387	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	562	32	287	38	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2644	14
	other conifers	42	75	0	.	72	100	85	72	0	.	14	74	44	79	0	.	529	44	2	100	0	.	0	.	0	.	254	.	0	.	0	.	0	.	0	.	0	.	0	.	8	93	5	100	0	.	8	75	26	42	11	74	77	99	279	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	0	.	349	98	12	72	-19	104	5	100	0	.	67	100	11	100	6	100	40	77	0	.	5	100	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	82	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	58	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	.	2227	26
	beech	1233	27	827	30	691	34	1902	30	329	48	418	41	2110	31	3054	18	4095	14	2993	18	267	52	756	38	1219	28	0	.	293	63	763	39	721	36	1277	35	0	.	747	35	638	46	468	39	14	76	716	35	0	.	3120	18	1369	28	868	37	1377	30	235	46	153	45	299	34	212	47	301	45	368	46	54	59	1285	32	751	42	524	56	937	32	918	32	1736	28	2014	22	1766	31	812	30	799	36	1313	28	364	37	321	67	704	35	559	33	357	39	803	33	0	.	154	67	0	.	853	33	882	34	711	35	1266	30	1046	31	555	43	750	42	32	100	48	72	396	63	62	81	436	47	362	64	71	73	1053	30	20	95	81	49	341	37	358	40	961	32	545	45	597	45	239	50	163	72	212	76	0	.	30	74	447	42	239	47	505	38	522	42	865	34	871	29	756	45	356	42	438	32	0	.	67072	3
	maple	369	30	183	36	55	79	35	73	25	75	202	50	731	23	480	28	363	24	549	27	144	49	117	54	229	41	0	.	60	74	83	51	284	36	91	56	3	100	276	31	130	42	115	79	37	68	114	45	3	100	428	27	188	33	51	51	303	33	100	57	176	43	84	64	142	88	35	52	138	44	35	78	230	57	168	53	123	53	338	39	225	49	580	32	103	53	168	45	179	40	101	39	287	42	132	45	109	47	74	70	320	54	57	78	108	51	34	100	102	72	12	100	43	71	24	100	23	90	22	42	90	72	32	73	158	45	63	75	2	100	139	60	167	44	175	43	37	66	9	78	108	50	0	.	31	63	38	52	80	37	83	54	110	48	38	71	10	100	0	.	73	64	11	88	54	64	266	31	85	52	216	43	68	54	354	28	113	48	244	55	70	60	249	51	0	.	13089	5
	ash	263	45	382	44	32	83	75	87	0	.	189	65	104	84	455	45	574	32	59	46	121	92	34	56	174	51	0	.	166	70	413	67	403	47	287	38	9	100	223	49	42	100	5	71	79	58	106	59	0	.	565	29	422	66	103	51	57	54	70	61	16	100	4	100	3	100	6	100	512	59	0	.	113	79	485	52	130	67	122	45	138	80	288	43	233	46	139	48	57	58	178	51	94	55	259	52	47	79	247	68	242	38	267	58	238	45	18	100	213	100	49	96	24	64	35	64	183	62	132	61	1	100	65	53	162	73	52	70	0	.	184	50	144	52	67	60	185	86	97	84	24	74	1	100	9	77	12	100	32	90	92	79	210	58	71	64	47	73	0	.	71	71	76	60	136	63	149	47	64	72	445	49	49	67	201	39	217	40	174	65	71	59	120	66	0	.	13113	7
	oak	353	55	81	71	0	.	396	48	0	.	0	.	33	61	69	73	558	23	0	.	79	100	4	100	109	57	0	.	9	100	62	100	51	80	113	62	391	58	0	.	0	.	64	100	0	.	36	100	87	100	333	45	90	91	178	66	41	100	54	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	29	100	45	100	2	100	17	71	0	.	212	47	13	100	11	100	31	85	160	52	20	100	0	.	0	.	108	53	557	74	307	48	0	.	0	.	0	.	28	66	99	81	224	42	50	100	92	58	103	61	51	100	0	.	0	.	0	.	0	.	0	.	97	100	2	100	9	100	129	71	82	56	0	.	405	50	25	100	11	71	234	49	0	.	42	100	42	94	7	100	31	53	102	46	12	100	17	100	50	58	44	72	100	85	177	41	112	55	94	68	0	.	7211	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	.	314	52	18	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	.	289	50	274	72	224	61	284	41	600	40	231	53	225	64	1120	36	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3688	16
	other broadleaves	277	45	174	36	142	44	98	67	0	.	72	73	412	23	214	27	580	27	80	33	99	52	53	59	57	50	0	.	23	87	232	55	76	45	187	47	14	65	154	47	113	47	48	81	311	43	571	34	464	54	363	36	145	41	64	60	109	45	0	.	26	55	7	100	0	100	24	60	91	51	6	76	38	50	3	100	36	73	157	39	136	70	116	40	39	48	95	69	5	75	214	51	18	77	100	52	63	54	99	66	257	55	140	43	197	61	97	64	397	51	127	72	206	37	393	31	873	31	503	39	256	42	369	38	66	48	144	57	8	72	17	67	304	94	85	60	32	57	195	84	147	64	179	98	4	75	24	53	117	72	47	60	118	86	46	65	123	75	0	.	13	100	314	34	327	49	289	28	7	63	174	57	161	64	74	46	21	59	43	44	132	62	115	58	0	.	13547	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
	total	3202	20	2273	22	2147	26	4650	20	3598	29	3879	28	20784	10	16028	11	16590	10	8736	14	832	36	1422	34	2824	25	254	.	1720	35	4754	22	5145	19	5362	20	494	54	4954	20	9519	14	4932	17	7644	16	14118	10	13596	10	10897	13	5153	19	6986	17	7406	16	1305	35	2852	25	1519	32	1370	36	3419	25	2838	27	2128	29	4436	21	3895	26	2477	32	4822	19	4150	26	7092	19	4554	19	3730	23	1751	28	1804	28	2740	24	3158	24	3647	23	2966	26	3052	25	2640	26	3231	21	1235	33	5027	31	2443	25	1851	22	1768	24	2731	21	4346	16	2750	21	3731	20	2422	29	1863	36	1013	36	2364	31	2781	27	3251	27	1669	35	745	45	3628	22	529	45	817	35	2824	21	2361	27	2635	24	2243	27	1415	32	1251	33	354	75	1114	46	9723	13	5965	17	8477	14	1904	32	2313	28	2081	28	4107	22	3818	21	1753	29	1318	30	1923	26	0	.	370019	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	354	37	507	36	881	42	914	38	2827	30	2131	30	16607	10	5702	14	4726	16	2814	17	45	81	293	58	439	49	0	.	793	46	2729	26	2770	24	2492	29	4	75	3676	23	8054	15	4175	19	7550	16	10765	12	6331	14	2265	23	1241	30	1959	24	3926	20	322	62	1571	26	523	46	552	40	1737	29	1743	34	1652	31	2571	26	1215	32	1793	34	2759	25	1963	34	3251	24	1414	28	917	43	301	44	274	54	363	50	1850	28	2401	26	1336	32	989	36	777	35	833	38	1163	38	3389	38	1540	30	54	100	83	71	31	100	462	50	215	63	616	37	939	37	1779	38	1794	30	1233	44	1931	28	1471	29	934	39	140	62	1236	31	142	68	255	51	2026	22	863	38	797	40	744	40	169	66	302	47	32	75	433	68	4580	18	4453	18	6636	15	1056	37	761	37	699	37	1401	31	1881	29	85	94	445	50	585	38	0	.	177433	2
	fir	252	72	43	91	147	60	975	34	621	41	1574	36	2304	24	6643	14	4883	17	2836	19	32	64	137	60	383	49	0	.	353	63	369	59	968	39	609	44	4	100	561	45	387	40	432	42	294	61	1015	38	0	0	3946	20	1601	30	3232	20	1590	26	531	53	1176	35	560	35	604	42	1790	33	373	56	201	62	1077	38	1030	37	94	64	727	41	780	47	1663	24	185	59	520	34	283	63	143	46	497	36	898	45	698	36	585	37	552	58	338	39	510	58	18	100	210	52	169	100	0	.	0	.	35	100	414	52	278	60	255	70	559	42	26	100	317	55	483	37	564	55	1153	40	205	52	76	100	1062	34	201	84	331	50	382	52	482	47	630	32	461	47	260	69	502	53	142	71	202	68	164	93	546	46	953	34	400	44	141	61	268	53	1037	32	426	34	95	85	19	100	87	58	0	.	65564	4
	pine	28	100	21	71	21	100	75	100	0	.	0	.	395	63	110	60	455	35	403	55	45	85	68	70	186	45	0	.	22	100	45	71	0	.	268	67	69	100	0	.	266	42	583	34	98	71	643	38	1218	27	388	37	18	100	230	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	178	100	42	100	41	100	0	.	58	60	0	.	54	100	334	39	101	63	99	53	4	72	120	51	72	84	8	100	0	.	85	68	118	77	203	66	0	.	48	74	89	79	0	.	4	100	0	.	0	.	0	.	14	100	0	.	41	100	224	60	0	0	0	.	0	.	0	.	30	71	10	100	0	.	20	100	0	.	23	71	0	.	30	100	0	.	0	.	69	100	0	.	873	31	439	29	635	33	0	.	25	100	237	46	92	60	193	42	156	48	87	73	57	100	0	.	10693	8
	larch	31	91	55	64	106	71	95	81	0	.	0	.	181	57	0	.	91	71	0	.	0	.	0	.	27	71	0	.	0	.	58	100	0	.	40	87	0	.	51	100	452	33	558	36	286	50	2623	16	5594	13	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	227	59	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	58	100	14	100	30	100	27	100	494	45	549	34	597	38	444	41	0	.	88	75	1673	23	557	35	602	56	0	.	0	.	0	.	242	45	0	.	20	100	0	.	67	100	0	.	23	100	0	.	0	.	0	.	0	.	57	73	0	.	22	100	0	.	68	100	5623	17	2727	19	1659	26	40	100	30	100	9	100	20	89	0	.	23	71	26	100	179	54	0	.	26705	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	69	0	.	9	100	263	46	1488	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	658	30	338	35	95	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2906	13
	other conifers	42	75	0	.	72	100	85	72	0	.	14	74	44	79	0	.	529	44	3	71	0	.	0	.	0	.	254	.	0	.	0	.	0	.	0	.	0	.	3	100	8	93	5	100	0	.	8	75	26	42	11	74	77	99	279	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	1	100	8	100	349	98	12	72	-19	104	7	79	0	.	67	100	11	100	6	100	40	77	0	.	5	100	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	82	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	58	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	.	2249	25
	beech	1233	27	827	30	691	34	1902	30	329	48	499	36	2915	25	3350	18	4348	14	3621	16	313	47	895	34	1354	27	0	.	293	63	763	39	1081	30	1277	35	0	.	953	31	679	44	616	32	25	61	724	35	0	.	3462	17	1568	26	868	37	1377	30	283	42	174	41	299	34	223	45	347	40	495	42	54	59	1330	31	797	39	945	44	1218	28	973	31	1866	27	2188	21	1766	31	812	30	916	34	1391	27	641	31	321	67	1089	35	597	31	357	39	803	33	0	.	185	58	0	.	893	31	1345	30	750	34	1266	30	1128	30	559	43	750	42	32	100	291	45	647	44	201	50	448	45	622	48	71	73	1140	28	169	89	81	49	341	37	358	40	961	32	551	45	597	45	239	50	262	59	212	76	0	.	30	74	902	31	320	44	505	38	522	42	894	33	894	28	756	45	356	42	438	32	0	.	75566	3
	maple	369	30	183	36	55	79	35	73	25	75	266	42	912	20	480	28	401	24	715	23	144	49	117	54	229	41	0	.	60	74	83	51	345	32	91	56	3	100	433	34	162	39	151	64	88	65	119	43	3	100	512	24	212	31	51	51	303	33	100	57	220	36	84	64	158	79	46	46	156	41	35	78	235	55	178	51	256	51	406	35	248	46	737	27	103	53	168	45	179	40	101	39	295	41	197	37	109	47	88	61	320	54	57	78	108	51	36	96	102	72	12	100	46	67	24	100	39	61	22	42	92	70	32	73	158	45	63	75	3	77	184	48	207	39	175	43	37	66	9	78	158	42	10	100	31	63	38	52	80	37	83	54	110	48	38	71	10	100	0	.	73	64	11	88	56	62	424	28	111	46	216	43	68	54	354	28	113	48	244	55	70	60	249	51	0	.	14914	5
	ash	263	45	382	44	32	83	75	87	0	.	259	55	189	63	455	45	651	31	92	46	138	81	34	56	174	51	0	.	166	70	413	67	489	42	287	38	9	100	262	44	60	77	10	61	185	55	106	59	0	.	775	29	422	66	103	51	57	54	70	61	17	94	4	100	3	100	6	100	512	59	0	.	117	76	587	45	270	48	143	41	138	80	368	38	233	46	139	48	57	58	178	51	137	49	336	46	47	79	247	68	281	36	267	58	238	45	18	100	213	100	49	96	46	48	35	64	207	56	132	61	25	94	111	45	162	73	57	64	0	.	184	50	147	51	67	60	324	65	97	84	24	74	1	100	9	77	12	100	32	90	92	79	210	58	71	64	47	73	0	.	71	71	117	49	139	62	238	45	116	60	445	49	49	67	201	39	230	38	174	65	71	59	120	66	0	.	14824	7
	oak	353	55	81	71	0	.	396	48	0	.	0	.	33	61	69	73	608	22	0	.	79	100	4	100	109	57	0	.	9	100	62	100	52	79	113	62	391	58	0	.	0	.	64	100	0	.	36	100	87	100	333	45	90	91	178	66	41	100	54	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	29	100	52	89	2	100	17	71	0	.	212	47	13	100	11	100	31	85	160	52	20	100	0	.	0	.	108	53	557	74	307	48	0	.	5	100	0	.	54	51	125	66	482	33	85	65	137	44	103	61	51	100	0	.	36	100	50	74	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	405	50	25	100	11	71	234	49	0	.	42	100	42	94	47	65	115	64	116	41	12	100	17	100	50	58	44	72	100	85	177	41	112	55	94	68	0	.	7958	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	.	470	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	416	40	296	67	390	47	420	35	1202	31	471	52	259	56	1461	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	134	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5635	12
	other broadleaves	277	45	174	36	142	44	98	67	0	.	75	69	647	25	214	27	591	26	111	31	99	52	54	58	57	50	0	.	23	87	232	55	81	42	187	47	14	65	156	47	144	40	185	56	425	35	649	31	521	49	397	34	147	41	64	60	109	45	0	.	30	50	7	100	0	100	24	60	91	51	6	76	45	43	3	100	71	49	169	37	136	70	158	34	39	48	95	69	5	75	227	48	55	71	156	48	63	54	99	66	257	55	140	43	197	61	119	54	450	47	129	71	348	34	567	33	1255	25	523	38	342	36	567	34	66	48	144	57	37	65	53	69	314	91	95	55	40	50	195	84	147	64	179	98	4	75	24	53	117	72	47	60	118	86	46	65	123	75	5	100	13	100	386	29	427	39	406	25	7	63	174	57	161	64	74	46	21	59	43	44	132	62	115	58	0	.	15961	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	3202	20	2273	22	2147	26	4650	20	3802	28	4818	24	24239	9	17023	10	17283	10	10596	12	894	34	1602	31	2959	24	254	.	1720	35	4754	22	5786	17	5362	20	494	54	6095	18	10251	13	6778	16	8960	15	17423	9	15310	10	12112	12	5381	19	6986	17	7406	16	1410	34	3217	23	1519	32	1581	34	3994	22	3378	26	2128	29	5444	21	4243	25	3530	26	5689	18	4306	25	8097	17	4812	18	3730	23	1751	28	1934	27	3018	23	4175	22	3647	23	3505	24	3203	24	2640	26	3231	21	1880	28	5567	28	2889	22	2275	20	2603	21	4089	17	5131	15	3034	19	4320	19	2686	28	2144	34	2709	26	3077	25	3403	25	3428	26	2269	34	745	45	3847	21	855	47	817	35	2824	21	2361	27	2635	24	2291	26	1415	32	1251	33	553	60	1114	46	12496	12	9275	13	12201	12	2062	30	2313	28	2081	28	4165	22	3867	21	1753	29	1318	30	1923	26	0	.	420409	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. 2305678. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1948208