Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
volume of bolewood (HG 2010)
type of assortment · tree species (5 classes)
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
type of assortment	tree species (5 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	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	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	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	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	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	.	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
coniferous wood	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	59	100	0	.	0	.	93	100	0	.	0	.	7	100	0	.	80	100	0	.	0	.	0	.	36	100	0	.	0	.	0	.	3	100	21	100	0	.	0	.	0	.	0	.	17	100	19	100	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	167	94	9	100	0	.	0	.	13	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	106	72	98	81	0	.	104	78	0	.	19	100	0	.	0	.	1	100	0	.	29	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	2	100	7	100	92	94	0	.	0	.	230	65	1	100	0	.	0	.	0	.	0	.	0	.	0	.	1267	25
	fir	0	.	30	100	0	.	54	100	0	.	0	.	0	.	2	100	0	.	12	100	0	.	0	.	28	100	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	.	14	100	0	.	79	100	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	385	35
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	20	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	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	31	100	0	.	0	.	57	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	58	28	71	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	329	31
	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	.
	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	59	100	30	100	0	.	147	100	0	.	0	.	7	100	2	100	80	100	12	100	0	.	21	100	84	100	0	.	0	.	0	.	6	100	21	100	0	.	0	.	0	.	0	.	17	100	19	100	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	167	94	9	100	0	.	0	.	13	100	0	.	0	.	15	100	0	.	103	80	0	.	0	.	0	.	145	59	98	81	0	.	104	78	0	.	26	100	0	.	0	.	57	98	31	100	29	100	0	.	78	53	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	9	100	47	58	121	85	0	.	0	.	282	59	67	100	0	.	0	.	0	.	0	.	0	.	0	.	1980	19
coniferous shortwood	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	305	40	443	43	522	49	408	39	1970	31	2138	30	14328	10	3714	17	2819	19	1693	23	2	100	117	69	265	51	0	.	421	51	2032	28	1900	29	628	49	0	.	1829	27	5962	15	3305	18	6190	16	8776	12	4726	14	126	59	1221	27	1437	31	4552	20	0	.	231	82	0	.	0	.	17	100	1022	39	1025	34	1433	28	228	54	1268	35	1724	28	935	47	2084	36	1276	26	810	43	276	41	274	62	234	55	1449	32	802	38	1303	32	476	49	407	48	859	37	883	36	1781	27	1804	29	10	100	21	100	20	100	347	55	78	67	372	41	813	40	1037	44	1367	31	513	40	1372	28	1234	31	625	46	137	50	517	45	107	73	9	100	877	31	266	60	459	44	898	42	279	58	292	67	0	.	7	100	3839	16	3337	18	4820	16	440	53	386	43	540	40	763	35	1149	30	495	58	159	59	430	37	0	.	122442	3
	fir	318	51	25	100	46	73	472	46	331	44	651	37	1601	24	3150	18	2419	21	1444	29	37	79	16	100	186	74	0	.	219	69	137	55	675	47	106	100	0	.	258	62	308	35	399	44	217	66	665	39	31	71	86	67	1076	34	1235	28	1330	25	0	.	0	.	0	.	0	.	0	.	237	59	158	62	617	46	44	74	122	62	273	57	360	56	529	45	72	68	335	37	310	57	156	47	222	40	615	46	440	45	469	37	218	45	164	49	86	100	7	100	104	63	153	100	0	.	0	.	28	100	252	57	245	59	139	69	486	43	34	100	286	53	69	50	475	55	887	39	60	69	52	90	275	51	2	100	62	100	117	68	15	66	214	46	0	.	235	79	242	72	0	.	0	.	98	80	408	47	585	40	227	68	40	71	45	100	425	47	246	43	42	82	0	.	137	86	0	.	29561	5
	other conifers	120	47	49	59	106	71	120	55	0	.	5	100	294	39	74	58	581	35	248	64	0	.	17	74	171	48	0	.	0	.	30	100	0	.	44	62	115	73	9	82	410	32	904	27	243	40	2531	15	5712	11	38	73	79	83	97	65	1	100	0	.	21	100	0	.	0	.	52	100	0	.	141	100	31	66	0	.	25	84	233	47	5	86	29	100	384	40	61	82	92	48	104	57	80	62	63	75	0	.	41	100	43	58	102	53	172	59	377	46	489	35	387	34	188	47	0	.	65	69	910	23	304	36	398	51	0	.	55	100	154	61	53	85	6	100	7	100	0	.	148	70	24	61	0	.	4	100	0	.	38	63	0	.	19	100	0	.	9	87	0	.	0	.	4895	15	1888	17	1989	18	46	100	16	100	167	48	113	48	123	50	151	52	148	68	150	68	0	.	26998	5
	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	.
	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	.	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	743	31	517	41	674	42	1000	34	2301	30	2794	27	16223	10	6938	15	5819	15	3385	20	39	74	150	62	623	44	0	.	639	47	2199	28	2575	29	778	44	115	73	2095	26	6680	14	4608	17	6650	15	11972	10	10469	10	250	47	2376	25	2769	25	5883	18	0	.	253	75	0	.	0	.	69	100	1259	34	1324	34	2081	29	272	50	1415	32	2230	24	1299	41	2642	31	1733	23	1206	32	679	35	534	43	536	37	2128	29	1242	34	1814	29	737	45	673	38	1117	33	1267	29	2374	25	2343	25	199	47	21	100	113	53	1509	23	627	34	909	36	1300	33	1126	41	1807	29	635	38	1853	28	2128	31	685	45	337	50	815	38	109	72	74	95	993	30	318	56	673	39	917	41	514	60	543	53	0	.	7	100	8832	12	5633	15	7394	14	713	53	442	43	752	37	1301	31	1518	28	688	47	307	62	718	37	0	.	179001	2
coniferous mediumwood	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	.	59	65	0	.	155	57	0	.	29	100	775	41	1042	29	1229	33	549	39	0	.	184	90	0	.	0	.	0	.	175	69	533	40	516	48	0	.	1795	34	63	100	47	100	0	.	11	100	0	.	348	49	30	97	153	67	16	100	79	71	2	100	112	100	0	.	18	100	145	100	285	76	482	54	73	60	236	100	301	54	801	48	1260	33	0	.	0	.	0	.	0	.	25	100	497	51	830	43	38	100	6	100	158	100	0	.	2	100	168	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	308	73	266	100	417	77	117	71	27	100	70	73	0	.	31	85	0	.	0	.	0	.	60	100	0	.	99	71	0	.	0	.	91	79	15	84	1	100	392	44	524	59	148	66	388	62	2	100	57	66	0	.	0	.	260	81	21	75	0	.	16524	9
	fir	0	.	0	.	0	.	293	71	0	.	268	100	0	.	1255	31	989	38	349	41	0	.	105	71	76	63	0	.	0	.	0	.	270	55	195	75	0	.	177	78	0	.	8	100	0	.	48	100	0	.	403	35	174	75	288	70	0	.	100	70	12	100	67	78	6	100	58	86	25	100	62	76	178	57	175	70	0	.	142	55	100	73	617	40	0	.	0	.	0	.	0	.	43	64	7	100	156	53	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	333	45	0	.	0	.	35	100	0	.	65	79	0	.	0	.	104	100	38	100	0	.	107	81	0	.	0	.	223	77	57	100	0	.	0	.	97	70	60	70	113	59	56	100	173	54	70	100	0	.	20	100	13	100	0	.	8224	11
	other conifers	0	.	0	.	0	.	48	100	0	.	0	.	313	84	0	.	132	58	35	76	38	100	0	.	0	.	180	.	0	.	19	100	0	.	0	.	0	.	30	100	62	100	8	100	0	.	0	.	0	.	101	64	0	.	26	100	0	.	107	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	58	100	40	100	0	.	0	.	0	.	0	.	0	.	38	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	58	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	132	61	638	40	0	.	0	.	0	.	52	100	0	.	56	93	33	100	0	.	0	.	0	.	2404	20
	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	.
	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	.	0	.	0	.	0	.	0	0
	total	0	.	59	65	0	.	496	54	0	.	297	100	1089	42	2297	26	2351	28	933	33	38	100	289	77	76	63	180	.	0	.	194	64	803	38	711	47	0	.	2002	33	125	71	63	79	0	.	59	100	0	.	853	33	204	77	467	62	16	100	286	59	15	100	185	76	6	100	76	89	170	100	346	65	660	43	250	65	236	100	501	46	942	42	1877	31	0	.	0	.	0	.	0	.	106	61	504	51	986	41	38	100	6	100	158	100	0	.	2	100	183	80	0	.	116	100	0	.	0	.	0	.	0	.	0	.	0	.	308	73	266	100	831	45	117	71	27	100	104	72	0	.	97	71	0	.	31	100	104	100	98	100	0	.	206	73	0	.	0	.	314	71	94	79	133	61	1030	35	621	51	208	65	501	61	110	100	230	55	127	72	33	100	280	76	34	82	0	.	27153	7
coniferous longwood	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	14	100	29	67	150	81	663	54	343	74	330	65	159	100	1145	33	783	45	948	33	14	100	0	.	124	100	0	.	254	74	308	65	600	63	365	50	0	.	75	92	0	.	0	.	0	.	0	.	0	.	1986	25	234	100	377	50	43	100	210	65	901	29	432	43	432	39	1434	29	0	.	0	.	0	.	569	47	0	.	296	68	178	100	78	100	287	73	0	.	200	100	0	.	20	100	0	.	244	76	0	.	159	62	330	66	359	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	74	0	.	0	.	105	100	0	.	645	40	37	74	267	55	960	29	330	56	353	45	155	96	0	.	113	97	0	.	343	72	0	.	0	.	246	74	260	75	0	.	69	80	299	65	470	58	179	100	163	82	131	85	0	.	20201	7
	fir	51	100	48	100	73	100	336	63	152	71	350	69	0	.	1126	32	470	44	812	35	0	.	0	.	0	.	0	.	2	100	173	94	55	65	385	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2836	23	0	.	1063	36	136	100	423	59	875	36	362	39	451	42	1038	34	0	.	0	.	1	100	597	41	0	.	0	.	18	100	222	71	46	100	0	.	4	100	0	.	63	71	0	.	14	100	28	100	210	82	67	76	282	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	455	47	194	69	225	50	73	59	297	53	235	49	202	58	47	100	153	69	77	100	77	100	0	.	0	.	4	100	31	100	0	.	82	75	230	57	40	76	0	.	0	.	13	100	0	.	15205	8
	other conifers	16	100	18	100	19	96	9	100	0	.	18	92	24	100	12	100	161	52	80	58	0	.	0	.	0	.	0	.	17	100	173	78	0	.	263	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	46	0	.	181	79	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	306	89	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	40	100	84	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	198	90	0	.	0	.	78	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	13	91	37	100	0	.	13	100	21	100	0	.	2105	22
	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	.
	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	0	.	0	0
	total	81	83	95	61	242	63	1007	42	496	71	698	52	184	100	2283	28	1414	32	1841	27	14	100	0	.	124	100	0	.	272	75	654	57	655	61	1013	43	0	.	75	92	0	.	0	.	0	.	0	.	0	.	5047	18	234	100	1621	33	178	100	633	49	1776	27	794	38	913	35	2472	23	0	.	0	.	1	100	1471	39	0	.	296	68	196	100	308	72	334	71	0	.	203	98	0	.	82	73	0	.	264	73	28	100	409	52	481	61	642	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	71	0	.	0	.	105	100	0	.	1100	34	243	60	492	45	1034	29	628	48	587	38	556	51	47	100	266	58	155	100	419	71	0	.	0	.	250	74	291	72	0	.	159	54	543	52	547	57	179	100	175	83	165	82	0	.	37512	6
broadleaf roundwood	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	.
	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	1422	26	775	30	574	34	1744	29	272	44	497	33	2431	25	2805	17	4036	14	2986	15	434	43	963	34	1714	30	0	.	231	55	723	36	928	33	985	34	0	.	889	29	569	44	629	30	38	69	392	37	0	.	3027	16	1211	27	713	31	1175	29	434	51	191	34	236	35	204	41	376	35	395	42	32	58	1202	31	572	41	1065	41	971	27	1053	36	1639	25	1822	21	1290	30	961	27	899	33	1215	27	463	30	264	83	769	36	493	31	334	40	727	32	0	.	122	55	0	.	595	34	882	31	543	33	937	31	712	31	390	44	595	43	20	100	195	54	476	43	171	51	342	45	481	46	69	66	1015	29	172	93	102	51	283	34	328	38	950	32	431	50	544	44	154	54	249	52	236	61	0	.	23	69	813	28	261	43	379	37	361	39	783	30	861	28	618	46	431	40	366	30	0	.	65657	3
	other broadleaves	875	26	674	36	316	35	512	32	16	65	540	38	1613	22	821	28	1747	20	677	21	435	40	184	56	480	29	0	.	208	47	617	46	520	28	451	36	445	56	793	30	243	31	398	42	578	35	1199	24	368	36	1685	21	770	59	404	41	370	30	169	39	189	32	93	54	133	75	64	47	538	44	37	53	234	46	650	33	500	34	586	29	577	41	930	24	451	29	216	35	226	32	417	37	465	30	568	38	176	50	357	44	773	34	859	39	926	45	182	40	679	30	372	49	729	29	1142	23	2333	18	1045	28	669	30	1880	23	308	39	202	51	46	78	450	41	614	48	239	35	331	57	128	49	276	34	315	63	83	50	46	44	408	38	313	44	352	49	290	41	335	56	23	82	253	60	578	36	397	27	930	22	270	47	591	34	307	38	662	26	404	27	417	40	485	40	460	40	0	.	47617	3
	indeterminable	3	100	0	.	0	.	0	.	0	.	1	100	25	68	0	.	0	.	1	100	3	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	10	100	0	.	1	100	15	100	0	.	1	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	73	0	.	0	.	0	.	0	.	0	.	29	88	0	.	0	.	12	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	5	100	1	100	8	100	0	.	0	.	0	.	0	.	26	75	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	169	27
	total	2300	21	1449	29	889	30	2256	25	288	44	1037	32	4069	18	3626	16	5783	12	3664	14	872	37	1147	34	2205	27	0	.	438	49	1340	31	1448	26	1435	28	445	56	1690	23	822	34	1026	25	617	33	1606	21	368	36	4713	14	1980	30	1119	28	1546	25	603	41	380	30	329	34	337	39	440	31	933	34	69	40	1435	28	1222	31	1568	34	1557	24	1630	31	2570	21	2273	20	1506	28	1216	26	1316	29	1680	24	1043	30	440	54	1126	32	1266	28	1194	34	1653	31	182	40	801	27	372	49	1325	23	2024	21	2882	16	1983	21	1389	22	2270	21	904	36	222	53	240	50	952	33	784	40	583	35	812	43	197	47	1291	26	487	52	185	44	329	32	736	28	1263	28	783	37	835	36	488	54	272	51	489	55	578	36	420	26	1744	20	531	40	970	31	668	29	1445	25	1264	26	1035	33	916	35	826	29	0	.	113443	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	377	36	530	36	672	42	1318	31	2314	29	2497	27	15270	10	5901	13	4911	15	3190	17	16	87	301	61	425	44	0	.	674	42	2515	24	3036	23	1531	28	0	.	3699	21	6025	15	3352	18	6207	16	8806	12	4726	14	2461	21	1485	27	1968	25	4610	20	303	49	1134	28	544	40	432	39	1470	28	1335	33	1319	31	1915	25	870	34	1517	33	2320	23	1914	32	3424	25	1564	25	810	43	476	48	274	62	278	48	2053	26	1973	26	1342	31	745	36	895	37	1238	30	885	36	1949	26	1805	29	10	100	50	72	20	100	357	54	78	67	372	41	813	40	1345	37	1633	30	934	41	1489	26	1260	31	800	39	137	50	1193	29	144	58	275	54	1837	21	657	38	812	32	1152	36	303	54	405	55	91	79	367	68	3847	16	3821	17	5590	15	848	38	1004	33	612	36	1120	30	1619	27	673	50	581	46	583	33	0	.	160435	2
	fir	370	46	103	60	119	67	1156	32	484	37	1270	34	1601	24	5533	14	3878	17	2617	20	37	79	121	63	291	51	0	.	221	68	310	58	1002	35	686	47	0	.	435	49	308	35	407	43	217	66	714	37	31	71	3325	20	1250	31	2586	21	1466	25	524	50	888	35	429	35	457	42	1095	33	263	54	219	49	796	38	815	34	122	62	415	42	478	45	1382	27	118	57	415	35	314	57	156	47	328	31	660	43	610	35	497	35	428	46	231	41	375	58	7	100	119	56	153	100	0	.	0	.	28	100	263	55	245	59	139	69	519	41	34	100	286	53	401	38	475	55	887	39	94	57	52	90	795	33	196	68	286	45	294	47	350	46	449	34	309	47	283	68	395	52	300	63	140	68	98	80	408	47	686	35	319	51	153	47	249	46	829	31	356	37	42	82	20	100	163	73	0	.	53375	4
	other conifers	136	43	67	51	124	62	176	46	0	.	22	76	632	46	86	52	874	27	364	46	38	100	38	65	190	45	180	.	17	100	221	63	0	.	307	57	115	73	38	80	472	31	912	27	243	40	2531	15	5712	11	364	34	79	83	303	52	1	100	107	100	21	100	6	100	29	100	52	100	0	.	141	100	31	66	308	88	25	84	291	43	45	89	37	82	384	40	84	66	92	48	104	57	118	48	63	75	7	100	41	100	83	57	186	49	172	59	377	46	489	35	443	33	336	44	0	.	65	69	967	22	304	36	398	51	0	.	55	100	154	61	171	41	6	100	7	100	0	.	148	70	24	61	12	100	34	90	0	.	38	63	0	.	217	82	0	.	9	87	78	100	21	100	5067	14	2554	16	1989	18	46	100	68	80	228	42	126	44	216	41	184	46	161	63	171	61	0	.	31836	4
	beech	1422	26	775	30	574	34	1744	29	272	44	497	33	2431	25	2805	17	4036	14	2986	15	434	43	963	34	1714	30	0	.	231	55	723	36	928	33	985	34	0	.	889	29	569	44	629	30	38	69	392	37	0	.	3027	16	1211	27	713	31	1175	29	434	51	191	34	236	35	204	41	376	35	395	42	32	58	1202	31	572	41	1065	41	971	27	1053	36	1639	25	1822	21	1290	30	961	27	899	33	1215	27	463	30	264	83	769	36	493	31	334	40	727	32	0	.	122	55	0	.	595	34	882	31	543	33	937	31	712	31	390	44	595	43	20	100	195	54	476	43	171	51	342	45	481	46	69	66	1015	29	172	93	102	51	283	34	328	38	950	32	431	50	544	44	154	54	249	52	236	61	0	.	23	69	813	28	261	43	379	37	361	39	783	30	861	28	618	46	431	40	366	30	0	.	65657	3
	other broadleaves	875	26	674	36	316	35	512	32	16	65	540	38	1613	22	821	28	1747	20	677	21	435	40	184	56	480	29	0	.	208	47	617	46	520	28	451	36	445	56	793	30	243	31	398	42	578	35	1199	24	368	36	1685	21	770	59	404	41	370	30	169	39	189	32	93	54	133	75	64	47	538	44	37	53	234	46	650	33	500	34	586	29	577	41	930	24	451	29	216	35	226	32	417	37	465	30	568	38	176	50	357	44	773	34	859	39	926	45	182	40	679	30	372	49	729	29	1142	23	2333	18	1045	28	669	30	1880	23	308	39	202	51	46	78	450	41	614	48	239	35	331	57	128	49	276	34	315	63	83	50	46	44	408	38	313	44	352	49	290	41	335	56	23	82	253	60	578	36	397	27	930	22	270	47	591	34	307	38	662	26	404	27	417	40	485	40	460	40	0	.	47617	3
	indeterminable	3	100	0	.	0	.	0	.	0	.	1	100	25	68	0	0	0	0	1	100	3	100	0	.	11	100	0	.	0	0	0	0	0	.	0	0	0	.	7	100	10	100	0	0	1	100	15	100	0	0	1	100	0	0	2	100	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	3	73	0	0	0	.	0	0	0	.	0	.	29	88	0	.	0	.	12	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	5	100	1	100	8	100	0	0	0	0	0	.	0	0	26	75	0	0	1	100	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	0	0	0	1	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	169	27
	total	3183	19	2150	24	1805	26	4906	18	3085	27	4826	22	21571	9	15146	10	15446	10	9835	12	963	35	1608	32	3111	24	180	.	1350	34	4387	21	5486	20	3959	20	560	52	5862	18	7627	13	5698	16	7284	14	13656	10	10836	10	10863	12	4794	19	5976	18	7622	16	1536	31	2424	24	1307	32	1256	32	3057	21	2530	25	1749	29	4177	20	3215	25	3232	24	4583	18	4066	24	7413	18	4339	18	2815	24	2098	25	1849	27	2404	23	3819	21	3030	23	3007	24	2523	25	2506	27	3438	22	1451	26	3358	20	2773	23	1671	21	2075	21	2995	16	3570	16	2016	20	3179	18	2236	27	1656	33	2314	27	2458	24	2755	24	2737	28	1706	35	534	43	3303	21	838	47	782	36	2460	20	1780	26	2524	22	2462	25	1420	34	1297	37	741	54	1018	45	9591	11	7204	13	10010	12	1744	32	2195	27	1757	25	3519	21	3456	21	1935	29	1678	30	1742	26	0	.	359089	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. 2298149. Birmensdorf, Swiss Federal Research Institute WSL

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