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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NFI1
growing stock (stemwood)
ownership (2 categories) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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	707	36	581	34	734	32	2097	22	676	41	895	34	6580	13	3208	16	3757	16	2312	19	150	85	192	51	219	40	0	.	1013	31	1038	27	2583	21	1247	29	2	75	2824	19	3586	17	3146	16	5316	14	8625	10	4777	12	2241	17	1051	29	766	39	884	29	275	41	212	36	372	38	366	39	686	33	634	34	2006	21	1623	21	683	43	1099	36	3097	19	983	30	1117	31	1282	24	1120	28	482	37	655	40	234	46	1308	25	2024	22	1077	28	472	50	336	58	452	35	689	30	1285	21	1133	23	-1	100	106	74	20	100	171	52	55	67	251	52	500	38	605	42	1232	26	567	43	625	34	696	35	989	32	127	76	768	27	266	47	548	32	1178	21	509	30	919	26	842	25	88	57	473	53	210	41	872	39	3298	13	2838	15	4414	14	827	33	340	36	773	36	279	55	849	31	649	35	586	42	519	38	0	.	114905	2
	fir	332	35	121	39	200	50	1232	24	229	54	449	38	714	24	1687	20	1760	20	1550	19	33	59	123	70	139	53	0	.	186	50	58	75	960	30	410	44	0	.	476	40	213	44	506	29	210	55	438	33	0	.	2089	17	247	39	250	52	181	61	400	43	253	47	346	37	368	33	585	30	210	47	326	46	259	32	277	38	248	40	323	40	171	45	236	40	74	61	414	40	398	35	246	48	225	34	280	42	265	38	450	32	210	53	15	84	43	100	0	0	147	66	87	71	0	.	0	.	6	100	229	52	283	41	183	63	246	54	0	.	134	54	209	36	231	53	214	39	187	51	0	.	415	35	115	67	667	34	161	43	289	38	371	27	348	36	177	60	168	56	144	59	51	68	153	50	206	46	428	27	340	32	316	65	76	63	296	53	197	54	30	91	0	.	167	61	0	.	28683	4
	pine	166	39	341	45	75	41	220	45	0	.	5	100	303	56	14	97	268	43	179	50	34	65	150	64	232	44	0	.	26	78	113	56	0	.	119	75	14	100	0	.	304	38	664	27	0	.	602	32	1035	19	470	29	0	.	55	76	21	78	70	71	0	.	35	100	6	100	17	100	0	.	73	71	9	81	84	66	0	.	112	63	1	100	8	100	265	36	119	43	185	38	116	49	216	35	145	53	7	100	24	100	63	66	71	62	160	57	13	74	190	56	29	100	0	.	0	.	0	.	0	.	0	.	1	100	10	100	0	.	122	47	3	100	0	.	0	.	0	.	0	.	9	100	98	73	16	73	0	.	12	63	0	.	149	78	16	100	0	.	32	100	0	.	987	25	221	29	437	30	1	100	0	.	83	49	45	60	224	43	143	57	111	54	143	65	0	.	10287	7
	larch	9	83	123	45	23	61	102	58	0	.	0	.	111	50	20	100	101	52	20	100	0	.	0	.	16	71	36	.	92	100	78	71	0	.	0	.	0	.	22	100	257	34	270	33	122	42	1303	16	3460	12	23	72	2	100	2	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	6	100	29	100	1	100	288	50	23	100	0	.	62	67	9	100	2	100	1	100	0	.	0	.	0	.	40	72	0	.	0	.	0	.	248	48	344	24	94	35	104	49	35	68	85	93	648	23	302	38	292	44	0	.	0	.	37	100	39	56	11	89	0	.	32	100	0	.	0	.	0	.	12	100	0	.	1	100	0	.	38	71	0	.	0	.	0	.	0	.	2777	13	1338	17	950	20	30	100	0	.	0	.	0	.	0	.	54	74	78	77	85	61	0	.	14309	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	130	42	1114	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	11	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	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	388	25	139	30	67	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1887	12
	other conifers	3	100	0	.	6	100	25	70	1	100	0	.	0	.	0	.	37	64	16	100	0	.	0	.	0	.	17	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	49	1	100	14	100	59	99	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	2	100	0	.	16	100	1	100	4	87	4	90	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	63	0	.	1	100	0	.	0	.	0	.	2	60	0	.	1	100	0	.	0	.	0	.	96	86	0	.	5	100	0	.	0	.	2	100	0	.	0	.	0	.	-1	100	17	72	0	.	10	100	0	.	0	.	0	.	0	.	411	29
	beech	1564	19	717	24	677	28	739	26	55	64	187	43	922	25	841	26	2972	16	1984	15	458	40	764	29	1024	24	0	.	188	55	206	44	552	32	534	36	0	.	746	29	254	43	299	34	42	59	251	41	0	.	2771	14	627	33	237	53	39	67	203	38	102	52	280	51	137	46	143	33	210	41	70	59	867	35	208	67	338	42	478	28	415	35	292	50	1050	17	994	24	1062	22	653	27	862	22	313	30	168	87	560	33	19	91	94	44	456	31	0	.	94	40	0	.	430	28	236	39	170	35	563	25	351	28	311	34	151	52	0	.	76	69	233	40	28	58	173	44	447	39	5	100	396	34	140	51	196	34	112	36	335	32	636	25	405	35	296	45	180	45	100	47	240	43	0	.	11	100	306	29	109	56	575	35	491	41	53	87	574	30	126	43	219	42	173	41	0	.	38566	3
	maple	236	25	40	38	49	49	80	44	8	100	55	71	145	29	55	38	144	37	225	25	46	64	105	40	60	38	20	.	2	100	4	71	55	42	19	97	0	.	129	32	22	51	13	60	7	64	35	40	0	.	267	25	79	62	2	80	12	62	26	63	9	65	53	54	45	86	9	71	53	53	45	75	35	40	52	78	49	56	174	42	71	47	57	62	48	38	85	60	73	42	43	52	126	40	57	47	3	78	34	72	22	101	31	46	10	100	17	100	1	100	0	.	5	71	0	.	1	100	18	100	5	100	0	.	25	62	0	.	0	.	22	67	32	73	39	34	86	44	0	.	23	58	19	100	7	75	25	57	22	46	64	40	78	46	5	73	1	100	7	100	-1	100	6	100	8	59	40	42	69	49	55	48	91	50	8	71	42	47	15	101	46	74	75	61	0	.	4082	6
	ash	258	26	236	45	172	37	79	47	0	.	53	69	49	53	108	41	267	36	117	43	135	49	10	100	35	68	0	.	52	78	17	71	142	48	17	76	0	.	48	50	1	100	0	.	14	60	7	89	10	100	242	29	38	49	0	.	0	.	0	.	30	78	5	100	1	100	0	.	88	61	26	58	17	100	137	42	89	42	48	47	17	71	18	100	146	37	30	65	20	68	31	51	73	59	61	58	0	.	34	72	3	72	110	48	61	52	3	149	5	100	15	81	17	71	2	71	33	66	8	91	10	82	13	59	1	100	3	100	10	100	66	62	1	100	19	74	45	46	59	99	24	72	109	68	0	.	1	100	50	69	71	70	104	52	2	100	11	72	1	100	90	63	10	100	1	100	54	52	126	48	134	53	133	44	10	64	48	51	60	76	62	66	71	53	0	.	4635	7
	oak	107	41	310	37	167	43	117	33	0	.	0	.	25	94	14	71	345	23	9	100	126	48	50	92	44	45	0	.	16	74	19	82	37	59	17	76	91	63	0	.	7	100	18	100	0	.	90	99	0	.	300	41	16	81	0	.	0	.	49	59	0	.	17	86	0	.	0	.	0	.	0	.	8	100	0	.	69	56	0	0	4	73	3	100	231	36	58	68	12	68	67	53	78	48	0	.	0	.	22	100	8	78	53	86	158	38	0	.	0	.	0	.	14	70	7	59	33	61	29	85	74	51	4	53	0	100	0	.	0	.	3	80	15	74	0	.	12	100	0	.	28	70	240	51	133	57	0	.	180	41	46	100	78	63	88	48	13	100	0	.	66	46	12	80	8	57	40	35	21	100	21	100	14	81	0	.	134	54	61	54	92	58	86	59	0	.	4315	8
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	58	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	154	70	140	57	100	60	0	.	355	34	223	64	117	40	100	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	31	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1380	18
	other broadleaves	113	39	209	39	106	45	48	84	1	100	15	74	160	38	34	42	223	31	87	32	119	47	79	78	53	33	0	.	9	100	33	65	16	55	60	82	0	.	40	41	36	45	17	49	98	43	239	25	29	58	166	27	44	66	0	.	38	68	32	70	6	59	16	92	9	81	19	67	70	84	27	53	39	51	3	100	98	55	117	41	75	49	5	68	24	52	51	43	38	45	87	51	91	44	41	44	12	54	28	68	94	64	35	51	111	45	59	62	101	33	42	63	141	41	133	36	91	44	223	30	104	51	169	33	7	91	22	100	12	53	38	84	41	54	24	67	35	62	24	89	22	57	111	64	26	79	5	49	36	58	17	55	21	94	24	67	27	89	1	100	22	68	163	34	45	42	194	37	7	70	57	60	62	51	1	100	34	72	75	63	48	83	97	49	0	.	5557	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	.
	total	3495	16	2679	18	2209	19	4739	15	971	35	1659	30	9013	11	5981	13	9875	10	6499	12	1102	28	1473	24	1823	20	73	.	1625	29	1566	25	4344	18	2422	22	107	59	4286	16	4685	14	4933	14	5809	13	11831	8	10475	9	8569	10	2120	22	1372	30	1176	28	1055	27	612	35	1125	28	951	29	1458	24	1265	27	2574	20	2885	18	1472	29	2004	26	4640	16	1762	24	1735	27	3198	16	2880	19	2275	19	1903	22	1904	20	2204	20	2478	21	2269	23	890	35	745	37	1451	25	1045	24	2322	16	1541	20	811	20	519	30	794	25	2112	15	1302	20	1324	23	941	31	630	41	1634	24	1180	27	985	29	1166	26	1832	26	215	64	1688	22	1098	28	1606	24	1482	19	1435	23	2125	19	2157	19	697	33	884	39	495	39	1339	29	7797	10	4814	12	6962	11	1530	27	1498	28	1741	25	692	43	2111	24	1213	29	1242	30	1415	27	0	.	229016	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	121	49	530	50	107	79	238	57	861	34	1906	23	8975	11	5127	13	4594	14	822	23	56	67	3	100	220	76	0	.	279	67	1330	29	965	25	902	29	0	.	285	55	1597	26	295	44	885	29	734	29	231	51	903	26	1182	31	1789	23	3047	19	12	100	1005	24	325	59	135	52	1289	27	340	44	113	92	510	61	1439	29	170	64	369	40	553	36	2083	24	102	57	137	73	6	100	0	.	125	52	406	46	916	34	370	48	1094	31	916	35	415	38	0	.	50	100	34	72	0	.	14	100	6	100	32	100	0	.	39	100	121	53	313	64	190	59	234	45	399	42	1053	26	433	48	393	57	167	41	98	80	0	.	567	33	213	54	96	86	112	67	13	100	97	62	0	.	345	66	423	39	280	43	631	29	352	55	548	49	758	35	834	27	785	29	228	49	422	40	464	54	0	.	61561	4
	fir	161	74	27	58	55	96	74	64	351	42	475	35	728	32	5316	13	2406	19	821	27	0	.	34	96	49	66	0	.	59	100	83	92	160	56	221	59	0	.	25	62	39	74	2	100	27	100	58	77	0	.	879	29	907	29	2013	18	1007	24	38	86	551	29	47	72	8	100	567	36	107	65	0	.	173	77	360	36	72	61	96	66	210	57	1158	30	0	.	99	84	36	79	31	77	203	41	149	50	285	39	32	71	217	53	152	53	119	78	0	.	0	.	0	.	0	.	0	.	11	100	0	.	17	100	0	.	85	63	0	.	9	100	128	64	3	100	441	36	61	69	0	100	24	70	218	63	0	.	40	85	10	85	18	77	21	78	0	.	44	100	0	.	33	93	116	100	21	100	82	63	83	52	7	100	271	38	898	27	463	41	23	94	183	67	8	100	0	.	24004	5
	pine	49	58	137	54	0	.	19	76	17	100	0	.	99	46	18	71	309	32	79	80	114	77	14	100	64	63	0	.	0	.	70	100	0	.	168	67	11	76	0	.	0	.	25	100	25	100	27	84	58	79	43	90	0	.	42	100	22	100	0	.	13	100	0	.	1	100	0	.	0	.	0	.	0	.	14	72	0	.	0	.	7	100	97	64	20	58	62	100	4	100	0	.	55	80	0	.	0	.	0	.	72	50	36	77	129	47	0	.	0	.	0	.	0	.	0	.	54	100	0	.	0	.	44	100	25	100	0	.	11	100	0	.	0	.	0	.	0	.	6	100	0	.	4	100	0	.	0	.	0	.	0	.	18	100	1	100	0	.	0	.	0	.	171	42	112	39	50	47	0	.	29	71	192	48	85	49	199	56	157	56	137	58	51	66	0	.	3266	11
	larch	35	88	8	100	0	.	34	72	7	100	44	100	36	100	16	100	12	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	86	0	.	0	.	146	53	104	50	0	.	86	39	78	55	0	.	75	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	91	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	12	100	0	.	0	.	0	.	32	100	0	.	0	.	-4	100	10	100	5	100	9	100	0	.	0	.	0	.	36	85	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	782	29	430	30	205	36	0	.	8	100	36	100	42	71	0	.	12	100	0	.	44	100	0	.	2524	13
	Arolla pine	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	63	46	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	44
	other conifers	2	100	0	.	0	.	0	.	0	.	2	100	1	100	0	.	111	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	1	100	0	.	1	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	83	100	1	100	-1	100	0	.	3	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	18	63	4	77	0	.	0	.	0	.	0	.	259	43
	beech	405	35	279	36	98	73	432	45	167	47	582	34	1176	25	1432	19	1491	17	510	35	157	65	238	54	343	41	0	.	154	72	432	39	306	42	607	39	3	100	221	57	280	69	70	81	0	.	40	75	0	.	924	24	523	34	586	33	844	27	96	67	145	47	61	60	31	71	198	39	284	49	0	.	297	51	232	37	356	54	282	55	389	36	708	27	211	44	493	50	64	56	38	85	528	34	183	42	47	98	116	50	224	41	80	62	408	36	0	.	26	100	0	.	5	76	313	37	154	43	36	68	99	82	79	53	88	55	0	.	17	97	58	45	162	60	138	56	101	63	9	100	240	49	100	70	0	.	44	58	41	64	237	53	1	100	117	58	33	54	0	.	221	50	0	0	0	.	103	52	185	59	127	78	134	50	759	25	356	35	310	45	236	39	212	48	0	.	22510	5
	maple	127	55	23	100	0	.	14	100	28	61	91	42	361	25	88	34	94	51	94	40	34	67	60	55	4	99	0	.	25	100	36	70	49	50	9	80	7	100	105	41	37	65	0	.	14	66	0	.	0	.	205	27	54	44	27	64	91	36	9	75	27	65	0	.	0	.	8	76	86	51	0	.	26	53	114	34	48	60	48	50	43	54	294	25	6	79	1	100	19	69	5	119	81	40	37	49	47	44	2	76	154	51	12	71	28	84	0	.	0	.	0	.	22	94	12	72	21	60	-1	100	0	.	0	.	20	74	15	72	0	.	58	53	60	54	84	36	4	100	0	.	26	58	5	100	0	.	0	.	0	.	40	72	4	100	0	.	3	100	0	.	12	72	2	100	3	100	42	46	15	92	6	77	15	62	181	35	88	39	21	72	13	69	0	.	0	.	3548	7
	ash	60	52	99	51	0	.	0	.	100	86	154	39	75	44	212	39	234	43	11	93	15	96	7	100	25	86	0	.	15	100	58	69	67	43	229	42	4	80	105	64	31	83	0	.	60	71	1	120	6	100	380	30	109	48	85	40	72	41	63	84	0	.	0	.	0	.	18	100	74	57	11	100	13	85	265	36	35	61	9	79	147	60	120	35	0	.	9	100	23	100	25	100	66	52	68	61	13	76	57	71	95	36	129	49	86	65	7	100	0	.	0	.	0	.	14	130	90	38	14	67	0	.	17	71	25	97	21	95	0	.	26	61	16	91	22	93	93	60	23	100	0	.	18	100	0	.	0	.	0	.	84	58	8	71	6	63	87	65	0	.	39	80	24	60	5	76	39	51	95	67	36	65	10	86	162	37	101	39	39	86	61	47	38	56	0	.	4759	7
	oak	100	51	40	62	13	100	52	63	0	.	12	100	3	100	8	100	282	27	0	.	11	79	0	.	26	100	0	.	10	100	21	100	3	112	99	54	215	52	0	.	0	.	58	100	11	100	15	107	21	100	74	90	43	52	86	71	31	74	28	80	0	.	0	.	0	.	0	.	1	100	0	.	0	.	105	83	0	.	0	.	4	100	5	100	89	69	0	.	15	100	0	.	47	58	8	87	0	.	0	.	56	58	131	62	81	41	0	.	0	.	0	.	1	100	78	51	183	37	48	73	31	62	19	126	11	100	0	.	12	100	56	73	0	.	0	.	58	76	0	0	0	.	83	94	36	.	0	.	65	100	9	100	1	100	29	61	13	86	0	.	41	91	1	84	13	100	-2	276	0	.	0	.	0	.	15	100	0	.	48	56	38	65	66	75	0	.	2758	11
	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	.	98	47	24	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	51	15	90	74	47	516	28	530	26	326	51	103	46	622	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	14	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2503	13
	other broadleaves	265	54	99	46	4	100	5	100	27	66	8	76	211	29	110	32	64	36	32	38	23	61	27	88	5	71	0	.	76	70	31	81	17	45	26	61	21	71	62	51	14	72	28	70	61	68	122	44	22	48	207	31	44	40	37	63	23	57	44	79	1	100	0	.	0	0	14	74	26	66	14	100	14	65	1	100	57	51	55	57	54	66	84	40	2	100	0	.	1	100	37	100	30	72	47	63	18	61	59	68	67	53	59	69	8	63	0	100	18	55	47	83	120	52	175	32	447	28	99	37	66	50	93	57	37	83	7	72	19	76	33	52	72	76	14	48	15	93	0	.	16	95	0	.	1	.	1	100	1	100	1	100	67	97	11	79	32	81	0	.	5	100	75	43	46	54	168	52	13	60	16	58	15	68	57	43	8	53	44	50	23	49	64	77	0	.	4286	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1328	25	1242	30	277	53	867	35	1556	30	3275	21	11665	10	12325	10	9597	11	2369	20	410	50	381	46	735	37	0	.	618	43	2062	25	1568	24	2337	24	262	50	803	37	2145	23	589	33	1084	27	1181	23	441	36	3615	16	2938	22	4664	16	5137	15	290	56	1743	22	433	59	175	54	2095	24	919	33	137	79	1032	42	2614	22	738	39	915	32	1407	27	4553	17	440	40	801	46	168	49	137	71	1135	28	897	31	1327	31	636	40	2001	24	1528	28	1276	27	7	95	231	45	127	60	221	36	1121	22	1493	17	564	37	321	39	924	25	411	40	355	58	258	52	628	35	712	34	1787	24	765	43	431	54	473	36	527	49	37	.	652	33	330	46	484	42	277	45	176	50	308	47	0	.	696	42	1644	21	957	23	1325	22	743	39	778	40	1431	28	3051	18	2004	22	882	32	1112	32	948	37	0	.	132062	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	828	32	1112	30	841	30	2335	20	1537	26	2801	19	15555	8	8334	10	8350	10	3134	15	206	65	195	50	438	43	0	.	1292	28	2369	20	3548	17	2150	21	2	75	3109	18	5184	14	3441	15	6202	13	9359	9	5008	11	3144	14	2233	21	2556	20	3932	16	287	39	1218	21	697	34	501	32	1975	21	974	27	2118	21	2133	21	2122	24	1269	33	3466	17	1536	23	3200	19	1384	22	1257	26	488	36	655	40	359	35	1714	22	2940	18	1446	24	1566	26	1252	30	867	26	689	30	1336	21	1167	23	-1	100	120	66	26	81	203	46	55	67	291	47	621	32	918	35	1422	24	801	33	1025	26	1749	21	1422	26	520	47	935	23	364	41	548	32	1746	17	723	26	1015	25	954	23	101	51	570	45	210	41	1217	33	3721	12	3118	14	5045	12	1179	28	889	33	1532	25	1113	24	1634	21	877	29	1008	29	984	32	0	.	176466	2
	fir	493	33	148	34	255	45	1306	23	580	33	924	25	1442	20	7003	10	4166	14	2371	15	33	59	157	58	188	43	0	.	245	45	141	62	1119	27	630	35	0	.	501	38	252	39	508	29	237	50	496	31	0	.	2968	15	1154	24	2263	17	1189	22	439	40	804	25	393	33	376	33	1153	23	316	38	326	46	431	36	637	26	320	34	420	34	380	37	1394	26	74	61	513	36	433	33	277	43	428	26	430	32	550	27	482	30	427	37	168	49	162	63	0	0	147	66	87	71	0	.	0	.	17	74	229	52	301	39	183	63	331	43	0	.	143	51	337	33	233	53	655	28	247	42	0	100	438	33	334	48	667	34	201	39	298	37	389	26	369	34	177	60	212	49	144	59	84	55	269	52	226	43	510	25	424	28	323	64	347	33	1194	24	660	33	53	66	183	67	175	58	0	.	52687	3
	pine	215	33	478	36	75	41	239	42	17	100	5	100	403	43	31	58	577	26	258	43	148	61	164	60	296	37	0	.	26	78	183	51	0	.	287	50	25	65	0	.	304	38	689	26	25	100	629	31	1093	19	512	27	0	.	97	61	43	64	70	71	13	100	35	100	7	85	17	100	0	.	73	71	9	81	98	58	0	.	112	63	8	87	104	60	285	33	181	44	189	37	116	49	271	32	145	53	7	100	24	100	134	41	107	48	289	38	13	74	190	56	29	100	0	.	0	.	54	100	0	.	0	.	45	98	36	77	0	.	133	44	3	100	0	.	0	.	0	.	6	100	9	100	103	70	16	73	0	.	12	63	0	.	167	71	17	94	0	.	32	100	0	.	1157	22	333	23	487	27	1	100	29	71	275	36	130	38	423	35	299	40	247	40	194	51	0	.	13553	6
	larch	44	72	131	42	23	61	135	47	7	100	44	100	147	45	36	71	114	47	20	100	0	.	0	.	16	71	36	.	92	100	78	71	0	.	57	86	0	.	22	100	403	29	374	27	122	42	1389	15	3539	12	23	72	77	97	2	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	6	100	29	100	1	100	344	44	23	100	0	.	71	60	9	100	2	100	1	100	0	.	0	.	0	.	40	72	19	100	12	100	0	.	248	48	344	24	126	37	104	49	35	68	81	98	658	23	307	38	301	42	0	.	0	.	37	100	74	50	11	89	34	100	32	100	0	.	0	.	0	.	12	100	0	.	1	100	0	.	46	61	0	.	0	.	0	.	0	.	3559	12	1768	15	1155	18	30	100	8	100	36	100	42	71	0	.	66	63	78	77	129	52	0	.	16833	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	6	100	0	.	130	42	1114	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	11	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	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	418	24	185	28	67	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1969	12
	other conifers	6	72	0	.	6	100	25	70	1	100	2	100	1	100	0	.	148	51	16	100	0	.	0	.	0	.	17	.	39	100	0	.	0	.	19	100	0	.	1	100	0	.	1	100	0	.	0	.	24	49	1	100	16	90	59	99	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	.	0	.	83	100	3	82	-1	100	2	100	3	100	17	93	1	100	4	87	4	90	0	.	0	.	0	.	1	100	3	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	63	0	.	1	100	0	.	0	.	0	.	2	60	0	.	1	100	0	.	0	.	0	.	96	86	0	.	5	100	0	.	0	.	9	77	0	.	0	.	0	.	-1	100	17	72	18	63	13	75	0	.	0	.	0	.	0	.	670	24
	beech	1969	17	996	20	776	26	1171	23	222	39	769	27	2098	18	2273	15	4463	12	2493	14	615	34	1003	25	1367	20	0	.	343	44	638	30	858	25	1141	27	3	100	967	26	535	42	369	31	42	59	292	36	0	.	3696	12	1151	24	823	28	884	26	299	34	247	35	340	43	168	39	341	26	494	33	70	59	1164	29	440	37	694	34	760	27	804	25	999	24	1260	16	1486	23	1126	21	691	26	1389	19	496	24	214	71	676	29	243	38	174	37	864	24	0	.	120	38	0	.	434	28	548	27	324	27	600	23	450	28	390	29	239	39	0	.	94	59	291	33	190	51	311	35	548	34	14	74	637	28	240	41	196	34	156	30	376	30	873	23	406	35	413	36	213	39	100	47	461	33	0	0	11	100	409	25	294	42	702	32	625	34	811	24	930	23	436	35	456	28	385	32	0	.	61077	3
	maple	364	25	63	44	49	49	94	40	36	52	146	37	507	20	144	25	238	30	319	21	80	46	165	33	64	36	20	.	27	92	40	64	104	33	27	71	7	100	234	25	59	45	13	60	21	50	35	40	0	.	472	18	133	41	29	59	103	32	35	51	36	51	53	54	45	86	17	52	139	38	45	75	61	32	166	34	96	41	223	35	115	36	351	23	54	35	86	59	92	36	48	48	207	29	94	34	51	41	36	68	177	46	43	38	38	68	17	100	1	100	0	.	28	77	12	72	22	58	17	106	5	100	0	.	45	48	15	72	0	.	80	43	92	44	123	27	90	43	0	.	50	41	24	82	7	75	25	57	22	46	104	37	82	44	5	73	4	73	7	100	11	79	8	82	11	51	83	31	84	44	61	44	106	44	189	34	130	30	36	59	59	60	75	61	0	.	7630	4
	ash	319	24	335	35	172	37	79	47	100	86	208	34	123	34	320	29	501	27	129	40	150	45	16	72	60	53	0	.	67	64	74	56	209	35	246	39	4	80	153	47	33	80	0	.	74	58	7	81	16	72	622	21	147	38	85	40	72	41	63	84	30	78	5	100	1	100	18	100	162	42	36	50	30	68	402	28	123	35	57	42	164	54	138	33	146	37	38	55	44	62	56	53	139	39	129	42	13	76	91	52	98	34	239	35	147	44	10	83	5	100	15	81	17	71	15	117	123	33	22	54	10	82	30	48	26	93	24	84	10	100	92	48	16	86	42	60	137	43	82	77	24	72	127	60	0	.	1	100	50	69	155	45	113	48	9	53	98	58	1	100	128	50	35	52	7	64	93	37	221	40	170	44	144	42	172	35	149	31	99	57	123	41	109	40	0	.	9394	5
	oak	207	32	350	34	180	40	169	30	0	.	12	100	28	85	22	58	627	17	9	100	137	44	50	92	70	46	0	.	27	59	40	65	40	55	116	47	306	41	0	.	7	100	76	80	11	100	105	86	21	100	374	37	59	44	86	71	31	74	77	48	0	.	17	86	0	.	0	.	1	100	0	.	8	100	105	83	69	56	0	0	8	61	8	74	320	32	58	68	27	64	67	53	125	37	8	87	0	.	22	100	63	52	184	51	239	28	0	.	0	.	0	.	15	68	85	47	216	33	77	55	105	40	23	106	11	102	0	.	12	100	58	70	15	74	0	.	71	65	0	0	28	70	323	45	169	50	0	.	245	40	55	85	79	62	116	39	26	66	0	.	107	45	13	74	21	66	39	39	21	100	21	100	14	81	15	100	134	54	109	39	130	45	152	47	0	.	7073	6
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	209	38	50	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	292	44	155	52	174	40	516	28	884	21	549	40	220	30	722	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	5	100	0	.	0	.	14	100	0	.	39	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3883	10
	other broadleaves	378	40	308	31	110	43	53	77	28	64	23	55	371	23	144	26	287	26	118	26	142	40	105	62	58	31	0	.	85	63	64	52	32	35	86	60	21	71	102	35	50	38	45	47	159	37	361	22	51	39	373	21	88	38	37	63	61	48	76	54	7	51	16	92	9	81	32	50	96	64	42	49	53	41	4	80	155	39	172	33	128	40	89	38	26	48	51	43	39	44	124	47	120	38	87	39	30	43	86	51	160	43	93	47	119	42	59	62	118	29	89	53	261	32	307	24	539	24	321	24	170	36	263	29	44	71	28	78	31	51	71	51	113	52	38	46	50	52	24	89	38	52	111	64	27	76	6	44	37	57	18	53	88	77	35	52	58	60	1	100	27	59	238	27	91	34	361	31	20	46	73	49	77	43	58	42	42	59	119	44	71	58	161	43	0	.	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4822	13	3921	16	2486	18	5607	14	2528	23	4933	17	20678	7	18306	8	19472	7	8868	10	1512	25	1855	21	2558	18	73	.	2243	24	3627	18	5912	14	4759	16	369	39	5089	15	6830	12	5523	13	6892	12	13012	8	10916	8	12184	8	5058	15	6036	14	6314	13	1346	24	2355	18	1558	26	1127	26	3553	17	2184	21	2711	20	3917	17	4086	17	2742	22	5555	14	3168	18	6287	15	3638	15	3681	18	2443	18	2040	21	3039	16	3102	17	3805	17	2905	20	2891	19	2273	23	2727	18	1052	24	2553	15	1668	19	1032	18	1640	18	2287	14	2676	14	1623	17	2248	17	1352	25	985	33	1892	22	1808	21	1697	22	2953	18	2597	22	645	42	2161	19	1625	25	1643	24	2134	16	1765	20	2609	18	2434	18	874	28	1192	31	495	39	2035	24	9441	9	5770	11	8287	10	2274	22	2276	23	3172	19	3743	17	4115	16	2095	22	2354	22	2363	22	0	.	361078	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. 2293518. Birmensdorf, Swiss Federal Research Institute WSL

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