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	23-35calculated 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. “protection forest [2022]”) – is assigned to all measurement cycles.

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total wood volume (stemwood)
ownership (2 categories) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	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	.
public	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	236	41	521	41	842	43	1369	36	1203	40	1156	41	4048	21	2561	22	2005	24	2749	19	19	86	336	62	293	59	0	.	857	44	1598	34	1929	31	1254	38	0	.	3972	24	5105	19	2969	19	5307	19	10055	13	5988	14	2360	25	738	49	970	40	1294	33	196	51	373	42	425	51	363	40	733	43	922	45	1426	32	1601	28	214	54	1128	37	2172	28	1616	38	1135	46	1770	27	943	43	618	46	327	65	264	60	2144	29	1621	31	1114	39	130	71	396	49	651	42	946	42	2316	26	2096	31	12	100	27	100	0	.	390	52	102	68	406	45	636	53	749	50	1897	34	803	58	1220	35	350	56	603	51	37	100	1154	32	123	63	346	56	1662	24	664	40	940	32	1062	39	305	62	259	62	133	83	247	96	3920	18	4049	19	5775	17	600	42	653	38	524	45	336	50	498	55	404	88	611	53	513	36	0	.	121383	3
	fir	309	54	97	76	119	72	1223	35	267	58	928	47	1030	34	1716	30	1624	26	2225	23	37	89	101	70	261	66	0	.	99	83	288	62	673	40	601	57	0	.	495	52	309	35	475	43	335	68	876	39	33	71	2577	25	252	45	513	53	241	50	410	64	577	55	493	37	545	45	640	43	195	73	206	54	470	36	628	46	76	74	347	56	234	49	397	44	139	57	391	38	383	56	131	60	221	42	680	43	403	39	549	38	214	100	132	73	152	100	9	100	142	57	178	100	0	.	0	.	0	.	367	55	261	58	174	69	462	51	0	.	294	56	271	47	532	58	423	62	76	69	0	.	892	38	139	92	342	45	376	48	425	47	478	37	274	54	366	68	408	53	372	69	0	.	137	83	493	51	604	43	155	51	99	64	0	.	109	95	44	89	48	100	25	100	185	82	0	.	35474	5
	pine	19	100	35	71	29	76	72	87	0	.	17	100	413	76	5	100	408	41	408	55	0	.	26	76	171	55	0	.	18	100	38	73	0	.	195	92	85	100	0	.	199	46	563	39	0	.	610	35	1175	31	441	38	16	100	106	66	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	137	71	0	.	0	.	300	43	65	83	109	50	55	111	151	49	69	77	8	100	0	.	9	100	109	73	152	74	0	.	95	73	101	72	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	146	73	13	100	0	.	0	.	0	.	22	71	10	100	0	.	37	100	0	.	13	100	0	.	26	100	0	.	0	.	95	100	0	.	548	32	239	41	858	34	0	.	39	100	147	56	47	100	132	59	64	68	129	82	0	.	0	.	9373	9
	larch	9	100	52	89	67	98	112	85	0	.	0	.	75	67	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	0	.	0	.	67	87	335	47	473	43	224	57	2162	18	5190	15	20	100	0	.	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	0	.	211	58	45	100	0	.	33	77	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	0	.	0	.	515	46	542	33	256	41	418	44	0	.	77	80	1242	23	460	37	563	55	0	.	0	.	0	.	166	45	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	24	100	0	.	69	73	0	.	11	100	0	.	0	.	3265	15	1498	21	1282	23	55	100	0	.	16	100	2	100	0	.	50	71	20	100	101	60	0	.	20201	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	73	0	.	2	100	219	46	1334	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	555	32	203	44	112	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2458	14
	other conifers	36	77	0	.	43	100	42	69	0	.	0	.	0	.	0	.	150	74	1	100	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	11	80	31	40	-7	100	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	7	86	0	.	0	.	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	105	100	21	61	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	7	100	13	100	3	72	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1192	30
	beech	900	32	750	34	651	32	811	28	56	64	141	66	841	38	802	28	2547	20	3094	17	434	48	851	36	1407	34	0	.	238	72	259	43	548	38	260	66	0	.	750	32	274	44	420	33	34	66	489	44	0	.	2644	18	699	38	392	56	25	73	151	57	107	46	179	44	128	43	181	41	152	82	36	58	1341	34	340	56	98	60	478	38	965	38	376	63	1821	23	920	33	1051	28	972	37	885	32	438	33	253	88	770	43	172	57	215	59	444	49	0	.	131	63	0	.	794	33	477	53	209	50	977	31	900	31	510	43	137	48	0	.	179	69	440	53	100	65	339	52	105	73	15	100	822	37	122	94	109	49	291	35	387	44	935	33	227	54	351	61	46	67	270	50	38	100	0	.	20	74	560	38	125	65	356	44	337	52	38	74	505	41	225	55	226	41	314	34	0	.	44407	4
	maple	144	39	111	48	108	47	67	49	0	.	72	80	229	39	153	51	161	42	435	26	61	59	81	60	259	40	0	.	73	71	8	110	132	44	21	100	0	.	226	29	66	75	17	159	34	71	116	46	0	.	303	27	60	54	26	64	69	47	47	82	69	48	50	76	100	83	22	60	42	62	24	76	183	75	37	89	52	63	238	55	67	71	173	59	88	52	111	48	139	38	44	61	114	58	99	51	19	71	30	60	56	56	43	65	-7	170	12	100	71	73	19	100	2	100	0	.	22	72	12	45	26	100	4	100	69	65	4	100	0	.	70	53	67	60	58	60	17	82	0	.	99	49	0	.	15	83	16	63	35	81	40	60	73	50	13	100	16	100	0	.	-1	442	0	.	13	82	158	46	99	54	125	63	-28	148	64	70	66	75	203	55	114	68	235	55	0	.	6680	7
	ash	259	38	265	66	170	58	160	65	0	.	80	83	26	97	64	51	306	41	78	65	228	64	32	67	94	63	0	.	161	73	137	77	227	54	38	72	0	.	73	61	8	100	7	183	64	62	49	49	0	.	448	36	24	96	52	76	12	76	58	79	43	58	2	100	5	70	0	.	229	84	0	.	58	81	147	58	202	57	96	52	117	80	0	.	213	42	72	51	52	63	156	57	18	67	193	56	0	.	63	100	86	52	134	66	76	75	22	100	123	100	38	100	30	59	4	100	33	89	109	53	13	100	18	100	19	64	44	72	0	.	122	73	19	87	53	74	0	.	9	71	41	58	70	100	2	100	9	100	23	92	55	71	177	71	2	100	4	100	0	.	182	100	38	68	5	81	136	59	62	81	332	60	92	63	73	54	52	54	146	68	85	54	168	59	0	.	7489	9
	oak	134	74	0	.	0	.	156	76	0	.	0	.	6	100	60	71	312	33	0	.	79	100	0	.	132	55	0	.	17	100	0	.	37	68	8	100	168	70	0	.	0	.	59	100	0	.	22	94	0	.	175	46	7	100	79	84	0	.	40	97	0	.	33	100	2	100	0	.	0	.	0	.	0	.	0	.	46	72	1	100	23	70	0	.	159	61	0	.	22	71	18	95	140	51	0	.	0	.	0	.	25	94	77	62	119	76	0	.	8	100	0	.	41	53	0	.	74	96	104	74	78	56	68	90	10	100	0	.	0	.	16	73	26	100	0	.	9	100	0	.	40	84	114	100	95	58	0	.	206	67	0	.	38	78	97	71	25	100	23	100	0	.	12	100	16	52	61	50	10	100	51	81	13	100	20	100	126	64	138	47	166	64	93	64	0	.	3933	11
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	364	56	25	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	.	322	46	125	74	241	62	68	100	255	71	209	54	127	61	337	45	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	0	.	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2154	18
	other broadleaves	140	50	96	45	140	49	95	80	3	100	7	100	61	60	82	46	441	44	87	34	84	55	27	76	76	44	0	.	13	100	357	75	21	67	119	55	33	91	87	51	134	43	86	47	305	36	809	28	246	44	277	41	29	59	35	100	15	85	2	100	10	80	7	100	24	62	48	78	52	66	33	78	40	66	0	.	56	51	150	43	188	74	23	81	37	47	42	50	62	65	122	58	3	100	132	49	17	84	2	149	34	97	75	61	495	89	147	53	206	37	131	65	278	38	221	44	180	44	477	28	159	56	282	38	43	70	141	63	9	71	178	63	306	88	18	100	19	75	126	78	105	52	5	82	7	68	24	60	87	70	21	60	23	100	33	77	77	88	6	100	0	.	207	38	113	45	321	34	12	72	156	67	3	71	35	80	13	64	13	61	135	72	75	65	0	.	9949	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	2	71	21	72	0	.	0	.	1	100	0	.	0	.	0	.	0	.	2	100	1	100	0	.	0	.	0	.	9	100	7	100	5	100	19	94	33	64	28	79	2	100	4	100	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	4	73	14	100	0	.	0	.	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	3	73	11	100	0	.	22	100	0	.	2	100	22	93	0	.	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	16	95	49	57	8	83	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	391	20
	total	2185	24	1926	27	2175	26	4106	21	1529	39	2403	37	6748	19	5459	19	8103	14	9078	14	941	39	1454	33	2718	28	177	.	1479	38	2759	28	3569	23	2495	30	286	62	5681	21	6465	16	5075	17	6323	18	15814	10	14049	10	9239	14	1888	29	2386	33	1662	32	1010	40	1207	38	1196	36	1210	37	1674	31	1592	37	1886	31	3734	22	1408	37	1662	31	3843	22	3263	28	2103	36	4559	19	2552	26	2453	25	1863	30	1796	29	3768	24	2321	28	2583	29	737	47	1180	38	2084	33	1650	29	3957	20	2950	25	1816	22	796	37	850	36	3941	16	2136	22	2378	25	1398	39	938	42	2532	30	2101	31	2284	30	1251	47	832	47	360	58	3193	24	586	61	958	36	2378	23	1864	30	2468	25	2157	29	1169	44	847	39	900	55	466	69	8704	13	6712	15	9942	14	1119	35	1811	33	1114	36	724	48	1436	32	1311	37	1512	35	1685	29	0	.	265085	2
private	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	204	62	42	59	0	.	233	62	1399	42	1871	35	14909	12	4602	17	3841	20	872	35	0	.	0	.	163	79	0	.	28	100	1532	36	1790	34	599	38	0	.	426	57	2354	28	799	44	2497	29	1086	38	319	75	521	34	982	35	1253	33	4525	24	196	91	908	37	246	73	166	78	1086	39	637	46	235	.	640	54	686	47	487	64	701	40	607	53	2621	30	198	62	0	.	0	.	0	.	71	57	260	61	709	51	430	47	720	40	614	52	717	42	148	72	0	.	23	93	0	.	37	100	24	100	14	100	0	.	0	.	280	58	827	58	122	77	307	46	524	49	1146	35	397	60	150	59	191	51	15	100	0	.	511	44	170	100	11	100	207	79	41	72	226	100	0	.	158	88	728	44	657	33	839	39	280	70	573	58	169	56	1000	35	1361	32	412	62	98	72	101	88	0	.	71530	5
	fir	80	72	5	100	0	.	172	59	290	50	729	50	930	36	4862	17	3227	22	963	45	1	100	20	100	90	71	0	.	168	100	45	71	584	59	152	93	0	.	41	73	45	85	14	74	0	.	103	100	0	.	1252	33	1251	36	2629	23	1442	29	138	76	584	47	21	100	41	100	733	51	171	82	0	.	443	68	365	45	76	100	177	62	329	65	1224	33	0	.	127	100	0	.	77	82	154	50	22	100	367	59	64	72	321	43	166	46	314	70	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	174	65	25	100	109	88	210	61	9	100	641	50	13	100	65	88	55	100	97	100	0	.	3	100	1	100	27	100	105	91	0	.	0	.	0	.	166	69	0	.	17	91	200	57	246	72	75	80	315	47	931	31	409	38	11	100	0	.	0	.	0	.	28939	6
	pine	76	79	0	.	0	.	0	.	0	.	0	.	97	54	58	71	175	67	37	84	47	100	-17	100	34	74	0	.	0	.	169	100	0	.	157	79	43	81	0	.	16	100	75	86	48	100	39	79	109	74	6	100	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	66	88	34	100	0	.	0	.	0	.	0	.	0	.	0	.	80	65	50	71	45	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	38	100	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	419	56	241	38	48	71	0	.	0	.	100	68	64	54	119	59	106	74	66	100	45	100	0	.	2875	15
	larch	5	100	24	100	0	.	33	100	0	.	0	.	103	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	76	0	.	0	.	86	61	80	73	-3	100	303	45	108	58	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	14	100	0	.	10	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	220	70	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	2292	35	1055	34	277	56	0	.	22	100	0	.	0	.	0	.	0	.	0	.	76	100	0	.	4881	19
	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	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	86	173	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	56
	other conifers	0	.	0	.	0	.	0	.	0	.	8	78	41	93	0	.	213	59	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	99	-12	114	-10	160	0	.	0	.	57	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	3	71	33	74	6	100	0	.	0	.	0	.	0	.	652	50
	beech	638	50	122	60	0	.	1314	46	203	55	432	38	2072	32	2237	21	2022	21	515	38	38	100	198	95	538	51	0	.	61	84	525	45	486	50	1044	40	0	.	236	71	417	69	188	67	0	.	76	73	0	.	1028	35	700	39	431	39	1381	30	281	68	117	55	52	73	96	73	257	52	293	48	0	.	206	67	230	47	940	48	504	47	163	47	1346	28	313	58	457	62	103	.	133	76	574	50	99	65	15	65	198	65	385	37	191	54	323	34	0	.	0	.	0	.	0	.	747	41	438	43	0	.	0	.	26	100	483	54	25	100	53	77	160	76	89	71	65	63	353	59	61	76	346	50	6	100	0	.	20	72	19	100	183	95	298	90	227	71	130	71	0	.	226	71	0	.	0	.	389	48	164	56	81	79	81	58	877	31	430	41	490	62	265	65	104	80	0	.	31013	6
	maple	140	64	23	100	0	.	8	100	18	71	178	64	685	26	172	36	67	51	157	57	67	100	22	83	-1	711	0	.	0	.	46	70	170	53	45	53	0	.	163	51	70	67	156	97	34	100	0	.	0	.	160	35	96	48	42	60	240	39	32	72	106	63	0	.	0	.	11	70	105	38	0	.	77	61	150	47	27	191	124	55	155	58	454	31	0	.	5	100	7	.	32	77	104	49	104	59	79	49	22	93	208	66	58	83	82	68	0	.	0	.	0	.	19	100	50	72	21	64	0	.	9	100	2	100	78	58	33	100	0	.	121	76	151	52	69	58	10	100	0	.	36	78	0	.	0	.	12	100	10	76	48	75	16	100	0	.	0	.	0	.	9	100	2	100	24	90	154	41	22	100	51	76	59	61	247	37	44	64	0	.	18	100	0	.	0	.	6014	8
	ash	22	100	195	57	0	.	0	.	0	.	281	55	218	61	458	48	378	37	40	94	52	100	12	100	94	76	0	.	37	100	223	76	112	50	358	47	28	100	238	50	38	100	0	.	122	64	53	100	0	.	497	42	591	76	177	52	111	54	18	100	0	.	0	.	0	.	4	100	230	79	0	.	16	57	375	46	120	61	14	100	81	100	380	40	2	100	30	100	0	.	104	90	83	60	164	75	102	97	209	81	335	46	296	53	144	49	0	.	0	.	0	.	24	73	45	65	129	76	0	.	57	71	76	60	114	76	5	100	0	.	180	68	102	66	5	100	171	73	42	92	0	.	1	100	0	.	0	.	0	.	68	88	53	100	51	67	217	71	0	.	54	71	61	62	100	64	128	64	109	100	62	78	31	64	267	45	199	42	0	.	28	100	22	100	0	.	9441	9
	oak	153	91	62	72	0	.	190	64	0	.	0	.	22	71	0	.	298	30	0	.	0	.	3	100	0	.	0	.	0	.	54	100	0	.	55	83	351	77	0	.	0	.	0	.	0	.	0	.	72	100	155	94	111	80	101	100	36	100	14	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	32	100	0	.	0	.	0	.	0	.	88	92	0	.	0	.	0	.	26	71	16	100	0	.	0	.	94	58	410	83	170	49	0	.	0	.	0	.	14	100	122	57	392	35	14	100	50	61	15	81	34	100	19	100	31	100	0	.	0	.	0	.	62	100	0	.	0	.	72	100	0	.	0	.	208	71	20	100	4	100	159	63	0	.	0	.	25	100	16	74	81	84	25	48	0	.	0	.	29	71	11	100	0	.	17	100	16	100	0	.	0	.	3954	15
	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	.	79	100	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	.	39	100	0	.	178	100	224	75	634	31	1061	29	228	82	255	61	1386	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	130	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4263	16
	other broadleaves	155	64	26	69	0	.	12	89	1	100	59	79	802	35	84	32	226	45	17	71	6	100	12	86	12	100	0	.	27	85	11	60	19	54	23	64	1	100	125	63	17	59	62	63	168	75	102	57	188	68	90	52	106	63	33	62	75	47	0	.	22	65	0	.	0	.	0	.	30	100	0	.	0	.	6	100	22	61	46	70	51	100	120	40	3	100	0	.	0	.	67	100	13	100	45	58	1	1233	133	72	199	69	46	74	20	57	5	100	0	.	0	.	58	70	366	46	927	31	209	94	239	65	219	62	35	56	31	100	26	77	14	67	9	62	96	58	26	71	15	100	31	93	173	100	0	.	4	100	0	.	134	95	114	100	19	75	63	100	0	.	12	100	210	47	228	43	56	40	0	.	16	90	103	58	45	52	0	.	21	88	23	91	2	100	0	.	6810	10
	indeterminable	4	100	0	.	0	.	0	.	0	.	10	100	120	38	6	100	16	100	0	.	4	100	0	.	13	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	12	100	0	.	0	.	2	100	0	.	0	.	0	.	3	100	23	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	80	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	263	23
	total	1479	34	498	43	0	.	1961	37	1911	39	3569	28	19993	11	12478	13	10461	14	2602	28	215	78	251	87	943	46	0	.	320	74	2605	31	3162	32	2481	27	424	75	1231	39	3067	25	1373	37	2865	27	1848	30	814	45	3712	22	3840	26	4733	21	7833	19	679	51	1737	33	319	74	304	70	2100	31	1471	36	235	.	1381	48	2138	34	1690	37	1569	33	1386	44	6203	20	733	48	652	58	110	.	414	71	1024	40	711	45	1273	39	1057	42	2340	29	1876	38	1818	29	192	71	0	.	421	59	338	60	2002	27	3035	20	464	62	609	53	1724	32	1199	40	1020	51	377	67	1015	43	886	42	2021	35	1031	53	347	60	659	43	363	71	0	.	550	44	418	60	492	57	796	51	496	52	636	72	0	.	679	58	3784	25	2577	23	2249	26	821	54	881	54	890	37	3476	23	2567	27	1057	46	513	59	350	71	0	.	170825	3
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	440	36	563	38	842	43	1602	32	2603	29	3027	26	18957	10	7163	13	5847	16	3620	16	19	86	336	62	455	47	0	.	885	43	3130	25	3720	23	1853	28	0	.	4398	22	7458	16	3768	18	7803	16	11141	12	6307	14	2881	21	1720	29	2223	25	5819	20	391	52	1281	28	671	42	529	37	1819	29	1559	32	1661	31	2241	25	900	38	1615	32	2872	23	2224	31	3756	25	1969	25	943	43	618	46	327	65	335	49	2404	26	2330	27	1544	31	850	36	1010	37	1368	29	1094	37	2316	26	2118	31	12	100	64	71	24	100	404	51	102	68	406	45	916	41	1576	38	2019	32	1110	44	1743	29	1495	30	1000	39	187	51	1345	28	137	57	346	56	2173	21	835	38	951	32	1269	35	346	55	485	57	133	83	405	68	4648	17	4706	17	6614	16	880	36	1227	34	693	36	1337	29	1859	28	816	54	709	47	614	33	0	.	192913	2
	fir	389	45	102	72	119	72	1395	31	557	38	1657	34	1960	25	6578	14	4850	17	3188	21	38	86	120	61	350	52	0	.	267	70	333	54	1258	35	753	49	0	.	537	49	354	32	489	42	335	68	978	37	33	71	3829	19	1503	31	3142	21	1683	25	548	52	1160	36	514	35	587	42	1372	33	365	55	206	54	913	38	992	33	153	62	525	42	563	43	1621	27	139	57	518	38	383	56	208	48	376	32	703	42	770	35	614	35	535	47	298	41	466	57	9	100	142	57	178	100	0	.	0	.	27	100	367	55	261	58	174	69	636	41	25	100	402	47	481	37	540	57	1063	39	89	61	65	88	947	36	235	68	342	45	379	48	427	47	505	35	379	46	366	68	408	53	372	69	166	69	137	83	510	49	804	35	400	48	174	50	315	47	1040	30	453	35	59	83	25	100	185	82	0	.	64413	4
	pine	95	67	35	71	29	76	72	87	0	.	17	100	510	62	62	66	582	35	446	51	47	100	9	292	205	47	0	.	18	100	207	83	0	.	352	62	129	72	0	.	215	43	638	36	48	100	648	33	1285	29	447	37	16	100	170	56	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	137	71	0	.	43	100	366	39	99	64	109	50	55	111	151	49	69	77	8	100	0	.	89	59	159	55	197	59	0	.	95	73	101	72	0	.	0	.	0	.	0	.	0	.	15	100	0	.	54	100	184	62	13	100	0	.	0	.	0	.	36	58	10	100	0	.	37	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	967	30	480	28	905	32	0	.	39	100	248	43	112	52	250	42	169	53	196	64	45	100	0	.	12248	8
	larch	14	73	75	68	67	98	144	69	0	.	0	.	178	52	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	36	76	0	.	67	87	421	39	553	38	221	57	2466	17	5298	14	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	30	100	225	55	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	515	46	542	33	476	39	418	44	0	.	86	72	1242	23	460	37	563	55	0	.	0	.	0	.	180	42	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	24	100	0	.	69	73	0	.	11	100	0	.	29	100	5556	17	2553	19	1560	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	25082	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	73	0	.	2	100	223	45	1334	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	574	31	376	37	112	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2648	14
	other conifers	36	77	0	.	43	100	42	69	0	.	8	78	41	93	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	12	95	0	.	0	.	11	80	31	40	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-10	160	7	86	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	105	100	21	61	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	8	86	13	100	3	72	0	.	0	.	13	80	33	74	6	100	0	.	0	.	0	.	0	.	1844	26
	beech	1538	28	872	30	651	32	2125	30	259	45	573	33	2913	25	3038	17	4569	14	3609	16	472	44	1049	34	1946	28	0	.	300	60	784	33	1034	31	1304	35	0	.	986	30	691	45	608	31	34	66	565	39	0	.	3672	16	1399	27	823	34	1406	30	432	48	224	36	231	37	224	40	438	34	446	42	36	58	1547	31	570	38	1038	43	982	30	1129	33	1722	26	2133	21	1377	30	1154	27	1106	34	1459	27	538	30	268	83	968	37	557	31	406	40	767	32	0	.	131	63	0	.	794	33	1224	32	646	33	977	31	900	31	537	41	620	43	25	100	231	56	600	44	188	48	403	45	458	48	75	64	1168	30	129	90	109	49	311	33	406	42	1118	32	524	56	578	46	176	55	270	50	264	63	0	.	20	74	949	29	289	42	436	38	418	44	915	30	935	29	715	46	491	40	419	32	0	.	75420	3
	maple	284	37	133	44	108	47	75	45	18	71	250	51	914	22	325	31	228	33	593	24	128	59	104	51	258	41	0	.	73	71	54	61	302	36	66	48	0	.	389	27	136	50	172	89	67	61	116	46	0	.	464	21	156	36	68	44	309	32	78	57	175	42	50	76	100	83	33	46	147	32	24	76	259	55	187	41	79	77	361	40	222	46	627	28	88	52	116	47	146	36	76	48	219	38	203	39	97	42	52	52	264	53	101	55	74	76	12	100	71	73	19	100	21	92	50	72	43	48	12	45	36	79	6	77	148	43	37	90	0	.	192	52	218	41	127	42	27	64	0	.	135	41	0	.	15	83	28	56	45	65	88	49	89	45	13	100	16	100	0	.	8	140	2	100	38	65	312	31	122	47	175	50	31	176	311	33	111	52	203	55	132	61	235	55	0	.	12694	5
	ash	281	36	459	45	170	58	160	65	0	.	361	46	244	56	523	43	683	27	118	53	280	55	44	56	189	49	0	.	198	62	361	56	339	39	397	43	28	100	312	40	46	85	7	183	186	47	102	57	0	.	945	27	615	73	228	44	123	49	76	65	43	58	2	100	5	70	4	100	459	58	0	.	73	65	522	37	321	42	110	47	198	62	380	40	215	42	102	47	52	63	261	49	101	51	357	46	102	97	272	66	421	38	429	42	220	41	22	100	123	100	38	100	53	46	49	61	162	63	109	53	70	61	94	52	133	65	49	65	0	.	301	50	121	57	58	67	171	73	51	77	41	58	71	99	2	100	9	100	23	92	123	58	230	60	52	65	221	70	0	.	236	79	99	46	105	61	264	44	171	70	395	52	122	50	340	37	251	35	146	68	113	47	189	53	0	.	16930	6
	oak	287	60	62	72	0	.	346	49	0	.	0	.	28	60	60	71	610	22	0	.	79	100	3	100	132	55	0	.	17	100	54	100	37	68	64	74	519	57	0	.	0	.	59	100	0	.	22	94	72	100	330	50	118	76	180	67	36	100	54	76	0	.	33	100	2	100	0	.	5	100	0	.	0	.	32	100	46	72	1	100	23	70	0	.	247	51	0	.	22	71	18	95	165	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	8	100	0	.	55	47	122	57	466	33	118	66	127	41	83	75	44	81	19	100	31	100	16	73	26	100	0	.	70	88	0	.	40	84	186	72	95	58	0	.	414	49	20	100	43	71	256	47	25	100	23	100	25	100	28	60	97	71	86	38	10	100	51	81	41	58	31	74	126	64	155	43	182	59	93	64	0	.	7887	9
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	444	49	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	322	46	302	66	464	48	702	29	1316	27	437	50	381	45	1723	30	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	32	100	0	.	194	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6417	11
	other broadleaves	295	41	122	38	140	49	107	72	4	77	67	71	862	32	165	28	666	33	104	31	90	52	40	58	88	41	0	.	40	66	368	73	40	43	142	47	35	88	212	43	152	39	148	38	473	35	911	25	434	39	367	34	135	51	68	60	90	41	2	100	32	51	7	100	24	62	48	78	83	56	33	78	40	66	6	100	78	40	196	37	239	62	142	36	39	44	42	50	62	65	189	52	15	84	176	39	19	123	135	71	233	61	121	47	515	85	151	52	206	37	131	65	336	34	586	33	1107	26	686	34	398	45	502	34	78	46	172	55	35	59	193	59	315	86	114	51	45	52	141	71	136	46	178	97	7	68	28	53	87	70	155	83	136	85	52	56	140	66	6	100	12	100	417	30	341	32	377	29	12	72	172	61	106	57	81	45	13	64	34	58	158	63	76	64	0	.	16760	6
	indeterminable	4	100	0	.	0	.	0	.	0	.	11	85	141	34	6	100	16	100	1	100	4	100	0	.	13	100	0	.	2	100	1	100	0	.	10	100	0	.	9	100	18	73	5	100	19	94	35	60	28	79	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	4	73	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	7	100	3	73	11	100	0	.	22	100	0	.	2	100	30	73	4	100	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	21	76	49	57	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	654	15
	total	3664	19	2425	23	2175	26	6067	18	3440	28	5971	22	26740	9	17937	10	18564	10	11680	12	1156	35	1705	31	3661	24	177	.	1799	33	5365	21	6730	19	4976	20	710	51	6912	18	9531	14	6448	16	9188	15	17662	10	14863	10	12951	11	5729	20	7119	18	9494	16	1689	31	2944	24	1515	33	1514	32	3775	22	3064	26	2121	29	5115	20	3546	25	3352	24	5412	18	4649	23	8305	18	5292	18	3204	24	2564	24	2277	28	2820	23	4479	21	3594	23	3641	24	3077	25	3056	28	3902	22	1842	27	3957	20	3371	23	2154	21	2798	22	3885	17	4405	16	2745	21	4102	19	2598	28	1957	33	2910	28	3116	25	3171	25	3272	28	1862	36	707	41	3851	21	949	46	958	36	2928	20	2282	26	2960	23	2953	25	1665	34	1483	38	900	55	1145	44	12488	11	9289	13	12191	12	1940	30	2692	28	2004	26	4200	21	4004	21	2368	29	2026	30	2035	27	0	.	435910	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. 2282364. Birmensdorf, Swiss Federal Research Institute WSL

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