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
grid: 1.4-km grid
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	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	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	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	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	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	.	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	32	100	0	.	0	.	56	100	68	59	55	100	57	50	160	67	72	71	46	77	0	.	0	.	36	71	0	.	21	100	0	.	8	82	38	70	0	.	132	54	0	.	0	.	10	100	54	58	0	.	0	.	38	71	0	.	154	51	8	100	0	.	0	.	0	.	0	.	109	85	5	100	135	100	113	69	13	71	2	100	1	100	12	93	20	100	0	.	0	.	0	.	0	.	141	42	60	74	177	100	57	76	15	100	17	73	0	.	5	87	25	63	0	.	17	100	0	.	18	76	0	.	0	.	4	100	208	72	104	98	0	.	0	.	0	.	0	.	18	100	64	91	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	1	100	4	100	78	70	50	54	0	.	132	66	1	100	0	.	0	.	0	.	0	.	0	.	0	.	2665	15
	fir	0	.	18	100	0	.	33	100	2	100	0	.	0	.	52	98	1	100	49	87	0	.	0	.	16	100	0	.	0	.	0	.	25	77	21	90	0	.	61	75	0	.	0	.	0	.	0	.	0	.	0	.	45	76	12	100	61	90	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	30	78	11	100	0	.	19	87	50	85	0	.	45	100	0	.	0	.	0	.	63	65	21	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	22	62	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	94	0	.	3	100	0	.	6	100	0	.	0	.	1	100	0	.	0	.	4	100	0	.	0	.	39	75	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	799	19
	other conifers	0	.	0	.	0	.	0	.	10	73	0	.	0	.	0	.	0	.	0	.	0	.	12	100	11	100	0	.	7	100	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	46	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	11	71	0	.	0	.	0	.	0	.	18	74	14	100	0	.	0	.	0	.	57	86	1	100	0	.	1	100	0	.	11	100	4	100	4	77	31	100	41	60	4	100	62	100	148	46	12	72	42	90	33	100	0	.	7	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	60	63	83	47	137	99	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	925	21
	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
	total	32	100	18	100	0	.	89	100	80	60	55	100	57	50	212	73	73	70	94	68	0	.	12	100	63	79	0	.	28	79	0	.	33	63	72	64	0	.	193	55	0	.	0	.	10	100	100	42	0	.	0	.	82	72	12	100	215	53	8	100	0	.	0	.	0	.	0	.	121	77	5	100	135	100	154	58	24	78	2	100	19	87	62	87	38	75	59	79	0	.	0	.	0	.	261	40	82	63	177	100	58	75	15	100	31	61	4	100	10	68	56	62	41	60	21	84	62	100	188	45	12	72	42	90	57	74	208	72	111	92	0	.	0	.	0	.	0	.	18	100	89	71	0	.	3	100	0	.	18	76	0	.	0	.	15	95	0	.	0	.	5	100	63	61	162	46	225	65	0	.	161	60	38	100	0	.	0	.	0	.	0	.	0	.	0	.	4389	12
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	296	31	641	31	853	31	413	31	1398	27	1884	24	13022	8	4463	14	2464	16	1371	18	61	67	105	51	227	38	0	.	358	46	1921	23	1950	21	669	34	20	100	2146	19	4765	14	3891	14	6774	11	9059	9	4704	12	219	43	1229	21	1142	25	3504	16	0	.	129	81	0	.	18	100	85	89	774	31	1631	22	1837	19	549	34	1009	29	2287	19	857	33	1937	26	980	23	754	31	365	34	292	40	295	35	1671	23	1053	27	1028	26	421	36	392	40	598	34	790	25	1717	19	1660	22	6	100	119	84	11	100	387	36	46	63	386	32	735	30	775	38	1193	25	813	32	1431	21	2241	21	469	37	95	44	575	31	78	58	71	56	1025	22	535	44	540	30	792	30	177	51	332	44	70	62	179	50	4455	12	3457	14	4885	12	818	36	693	35	550	29	670	28	919	26	472	42	205	44	473	29	0	.	123349	2
	fir	342	35	49	78	167	46	399	36	326	36	423	34	1621	21	3569	14	2127	17	947	25	26	66	10	90	184	49	0	.	127	69	126	40	712	31	304	52	0	.	339	43	169	35	647	27	205	46	535	29	19	71	225	43	925	27	1294	21	1402	22	0	.	0	.	0	.	0	.	0	.	141	58	152	43	469	38	110	44	236	47	345	37	354	43	731	32	66	57	411	32	414	43	100	43	367	31	654	36	414	36	391	31	215	35	145	44	49	100	5	88	250	51	87	94	0	.	0	.	15	100	230	41	402	38	210	50	331	38	20	93	209	44	176	69	299	49	855	26	122	45	29	90	233	41	204	68	156	61	188	55	93	58	221	37	9	71	143	74	147	69	130	93	15	100	301	50	318	37	654	26	386	34	28	62	206	58	391	34	328	38	24	82	0	.	167	60	0	.	30564	4
	other conifers	221	38	163	47	155	40	200	44	20	100	3	100	352	26	53	49	455	28	213	49	131	78	75	68	147	40	0	.	0	.	17	100	0	.	41	54	65	73	5	83	463	26	961	19	237	30	2529	12	4963	10	30	60	120	65	59	61	62	58	40	100	12	100	0	.	0	.	30	100	0	.	121	75	71	62	73	92	41	58	425	33	6	67	93	60	302	33	75	51	155	34	60	53	175	40	84	65	0	.	29	85	30	51	71	47	233	42	361	31	659	24	377	27	164	35	39	61	35	68	956	18	249	29	293	39	0	100	30	100	140	52	45	65	27	64	47	80	3	100	165	57	72	82	14	100	2	100	0	.	48	62	0	.	32	54	1	100	53	91	0	.	5	80	5679	9	2175	13	1996	13	29	93	29	62	195	36	103	38	220	38	116	42	155	57	131	56	0	.	28508	3
	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	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	859	21	853	31	1176	26	1012	25	1744	25	2310	23	14994	8	8085	11	5046	13	2530	16	218	65	190	41	558	31	0	.	485	42	2064	22	2662	20	1013	31	84	61	2490	18	5397	13	5498	13	7215	11	12124	8	9685	9	474	35	2274	20	2495	19	4969	14	40	100	140	75	0	.	18	100	116	74	916	29	1904	21	2377	19	731	32	1287	27	3058	17	1218	29	2760	22	1348	19	1239	24	933	26	452	33	837	25	2409	21	1466	25	1448	25	667	33	608	32	879	28	1155	21	2626	16	2124	19	170	35	158	72	61	53	1573	16	697	26	888	26	1066	27	824	36	1542	23	1034	31	1757	21	3142	19	593	34	290	45	881	28	295	55	229	57	1213	21	677	38	761	27	833	29	321	54	532	38	200	78	198	47	10435	8	5950	11	7534	10	1233	30	751	34	951	28	1164	24	1467	23	612	36	360	41	771	27	0	.	182422	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	.	36	65	149	74	88	56	0	.	295	79	1200	24	828	24	1041	27	420	33	32	79	162	62	9	100	0	.	13	100	111	64	442	36	303	48	0	.	1022	34	978	30	59	71	35	100	577	51	400	31	217	44	81	81	89	67	9	100	105	65	2	71	77	85	0	.	11	100	96	89	167	76	299	48	42	60	138	100	331	40	468	48	1127	27	57	100	0	.	0	.	67	71	34	63	284	51	883	30	205	65	105	65	96	100	0	.	1	100	91	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	61	145	100	306	65	64	71	83	86	55	60	0	.	18	85	0	.	17	100	0	.	138	79	0	.	55	71	0	.	0	.	50	80	27	73	1	100	221	43	316	59	100	58	222	62	68	98	110	69	20	71	26	100	213	64	11	75	0	.	15761	7
	fir	0	.	0	.	67	84	168	71	0	.	331	63	228	78	1605	24	886	28	684	34	57	100	282	59	53	54	0	.	8	100	0	.	175	50	115	75	0	.	106	79	108	77	5	100	0	.	26	100	0	.	283	34	159	60	171	70	0	.	97	53	49	87	37	78	4	100	34	87	46	63	36	78	106	57	114	61	43	100	86	55	98	59	690	40	0	.	0	.	0	.	3	100	36	49	4	100	102	49	50	80	16	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	293	38	0	.	33	100	47	71	0	.	37	79	0	.	8	100	59	100	22	100	0	.	60	81	0	.	0	.	122	77	32	100	0	.	0	.	66	61	92	67	143	58	34	100	167	48	67	73	39	100	11	100	7	100	0	.	8516	9
	other conifers	0	.	0	.	0	.	68	71	0	.	0	.	194	79	0	.	108	45	46	66	83	72	4	100	36	100	102	.	0	.	33	76	0	.	0	.	0	.	18	100	40	92	15	75	0	.	65	74	966	19	99	56	0	.	14	100	0	.	58	100	14	100	30	90	0	.	0	.	0	.	0	.	0	.	1	100	0	.	31	100	23	100	0	.	0	.	0	.	0	.	22	83	31	60	0	.	0	.	0	.	20	77	0	.	0	.	0	.	0	.	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	52	0	.	0	.	0	.	0	.	0	.	0	.	23	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	73	61	403	37	0	.	0	.	0	.	42	79	16	100	39	78	40	71	50	70	0	.	0	.	2934	12
	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
	total	0	.	36	65	216	60	324	49	0	.	626	65	1623	26	2433	21	2035	22	1150	27	172	63	448	52	98	48	102	.	21	100	144	55	617	34	418	47	0	.	1146	33	1126	29	78	64	35	100	669	45	1367	19	599	30	240	64	274	62	9	100	259	51	66	89	144	62	4	100	44	90	142	75	203	65	405	40	157	59	181	80	448	37	589	40	1817	26	57	100	0	.	0	.	92	73	101	44	288	51	986	29	256	65	141	62	96	100	0	.	1	100	99	80	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	217	61	145	100	652	37	64	71	116	90	102	59	0	.	56	71	0	.	48	73	59	100	160	74	0	.	114	73	0	.	0	.	172	71	71	64	73	61	624	33	381	50	192	55	365	52	144	71	293	56	126	51	105	64	273	56	18	82	0	.	27211	6
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	7	100	59	63	89	82	573	41	222	67	467	41	278	58	925	28	788	32	892	28	8	100	0	.	67	100	0	.	279	53	234	55	760	39	519	35	0	.	45	92	0	.	0	.	0	.	0	.	0	.	2447	16	137	100	226	49	24	100	242	45	862	21	445	38	421	33	1522	21	0	.	0	.	0	.	489	38	122	100	161	68	154	71	270	84	294	52	73	71	152	81	0	.	34	75	0	.	141	76	62	100	398	62	197	67	282	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	75	0	.	0	.	120	64	0	.	823	37	90	53	197	46	878	24	393	43	293	36	245	50	0	.	186	74	17	100	312	56	0	.	23	100	271	48	164	69	0	.	41	80	174	64	295	56	108	100	143	65	334	60	0	.	20478	6
	fir	172	85	106	77	44	100	406	47	128	58	434	45	0	.	1026	26	431	34	1043	32	0	.	0	.	0	.	0	.	13	65	105	94	171	47	471	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2678	16	0	.	735	34	76	100	317	48	854	26	338	34	426	35	1304	24	0	.	0	.	1	100	497	33	38	100	0	.	42	80	205	58	33	83	0	.	106	80	0	.	43	61	0	.	8	100	16	100	227	64	40	77	171	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	99	0	.	264	46	151	55	272	37	98	47	263	45	201	39	281	44	26	100	128	51	77	71	113	61	0	.	0	.	5	71	17	100	0	.	44	75	210	51	34	59	0	.	0	.	7	100	0	.	14960	6
	other conifers	9	100	18	71	11	96	24	67	0	.	58	84	15	100	7	100	99	49	44	58	0	.	0	.	0	.	0	.	56	72	105	78	0	.	146	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	357	37	0	.	107	80	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	193	85	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	23	100	30	84	48	88	27	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	71	0	.	0	.	33	100	0	.	0	.	21	76	2	100	0	.	0	.	0	.	178	60	0	.	0	.	42	100	0	.	0	.	19	100	0	.	0	.	0	.	5	100	8	92	22	100	0	.	41	84	22	71	0	.	1816	17
	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
	total	188	79	184	54	145	63	1002	31	351	61	960	35	293	58	1958	23	1318	25	1979	23	8	100	0	.	67	100	0	.	348	49	445	52	931	35	1136	32	0	.	45	92	0	.	0	.	0	.	0	.	0	.	5483	12	137	100	1068	31	100	100	559	37	1716	20	783	33	864	28	2826	19	0	.	0	.	1	100	1179	32	160	100	161	68	196	64	479	67	327	51	73	71	258	58	0	.	77	59	0	.	154	73	101	85	654	45	284	61	480	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	71	0	.	0	.	217	62	0	.	1087	32	262	40	471	34	975	23	657	36	493	32	704	35	26	100	314	53	137	72	425	49	0	.	43	100	276	48	181	67	0	.	90	54	392	46	352	54	108	100	184	64	363	58	0	.	37255	5
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	1703	17	969	23	642	25	1605	23	283	34	825	29	2540	20	2452	13	3677	11	2845	12	421	38	853	27	1790	21	0	.	571	41	744	30	1071	25	1179	25	0	.	966	22	473	34	488	25	50	64	404	29	0	.	3553	11	1241	23	613	27	1048	24	431	34	277	36	303	33	215	32	360	26	400	30	98	54	998	29	629	28	819	32	881	21	1087	25	1429	20	1738	16	1369	21	959	21	896	24	1518	18	692	25	219	61	727	28	458	29	249	35	749	25	0	.	221	41	0	.	771	27	743	25	554	25	1043	21	774	28	657	26	380	41	11	100	119	52	422	35	132	42	400	32	597	31	55	49	944	26	326	44	124	34	267	28	283	29	995	25	528	31	443	37	234	42	183	44	335	39	9	100	41	72	754	21	319	36	549	32	720	36	808	24	812	22	579	37	355	34	405	30	0	.	67397	2
	other broadleaves	1375	21	716	25	462	22	539	22	152	44	418	30	1442	16	732	20	1732	14	746	19	424	31	433	35	430	24	0	.	238	35	406	42	472	23	678	24	384	42	729	23	216	24	320	33	583	23	1108	17	358	32	1922	14	620	43	413	28	329	23	205	33	117	30	120	46	181	62	80	38	396	37	97	40	305	35	715	24	433	26	501	22	562	28	640	22	509	24	296	29	284	33	321	30	565	24	516	28	170	35	329	33	728	27	711	29	769	35	284	30	736	21	422	30	843	22	1478	20	2306	13	1439	20	795	20	1482	18	204	36	123	46	82	45	362	34	494	37	381	25	398	34	112	40	256	29	632	33	200	43	43	34	344	31	365	34	405	33	233	35	268	45	16	66	361	43	571	24	434	21	1038	15	295	31	428	29	401	27	551	22	472	21	312	32	419	31	453	30	0	.	48364	2
	indeterminable	6	52	0	.	0	.	0	.	0	.	0	100	15	65	1	100	7	100	3	54	2	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	7	74	6	100	0	100	1	100	12	76	0	.	1	72	0	.	1	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	2	73	9	100	0	.	0	.	0	.	0	.	16	87	0	.	2	100	7	100	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	100	1	100	6	71	3	79	5	92	0	.	0	.	0	.	0	.	16	69	0	100	2	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	7	100	0	.	1	100	5	89	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	165	19
	total	3085	15	1685	20	1105	21	2144	19	435	30	1244	26	3998	15	3185	12	5416	9	3594	11	847	28	1286	23	2226	19	0	.	809	35	1150	26	1543	21	1857	21	384	42	1702	18	695	26	809	21	634	22	1523	15	358	32	5476	10	1861	23	1026	22	1377	21	639	28	394	29	423	30	396	34	439	23	796	27	194	45	1306	24	1344	21	1253	26	1391	19	1649	22	2069	18	2247	15	1665	20	1258	20	1218	22	2085	17	1216	21	389	38	1055	26	1186	22	961	26	1518	23	284	30	957	21	422	30	1615	17	2221	17	2866	12	2486	15	1574	18	2139	15	584	33	134	48	201	38	800	27	626	31	783	23	995	26	168	36	1200	23	958	30	323	32	310	26	627	22	1360	23	933	24	676	30	502	39	199	43	703	33	580	24	475	21	1797	14	614	28	977	25	1121	27	1359	20	1289	19	891	27	774	28	858	24	0	.	115926	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	336	29	735	28	1091	27	1131	25	1689	24	2702	20	14558	8	6375	11	4364	12	2728	14	101	48	266	43	338	33	0	.	671	33	2266	20	3160	17	1530	21	20	100	3344	16	5742	12	3949	14	6819	11	9691	9	5104	11	2883	14	1484	21	1456	21	3691	16	354	36	993	21	522	35	438	31	1618	21	980	28	1802	21	2271	18	1193	23	1282	27	2781	17	1479	25	3345	19	1352	20	827	29	516	34	359	35	362	30	2097	20	2138	19	1473	24	981	31	701	32	896	27	791	25	1813	19	1685	21	6	100	135	74	11	100	406	35	46	63	386	32	739	30	1199	29	1442	24	1121	29	1494	21	2324	21	644	30	114	40	1481	24	168	39	285	35	1902	16	1067	29	833	23	1092	25	191	48	517	39	137	44	519	38	4459	12	3780	13	5521	11	1081	29	1047	28	661	27	954	24	1234	23	606	37	561	33	818	29	0	.	162254	2
	fir	513	37	173	53	279	38	1005	27	456	30	1187	27	1849	20	6252	11	3446	13	2723	17	83	72	293	57	254	38	0	.	148	60	231	48	1084	23	910	31	0	.	506	35	277	37	651	27	205	46	562	28	19	71	3186	14	1129	24	2212	17	1539	21	414	39	903	25	374	31	430	35	1337	24	194	45	188	37	575	33	750	25	329	38	431	32	513	32	1675	22	98	47	456	30	520	38	102	42	447	26	720	33	545	29	457	28	458	36	185	39	223	59	5	88	257	50	87	94	0	.	0	.	15	100	252	37	402	38	210	50	351	36	20	93	209	44	469	35	299	49	887	25	232	39	29	90	559	28	355	46	440	32	345	37	384	34	422	27	350	38	170	64	275	44	330	49	164	47	301	50	318	37	764	23	495	29	171	50	321	42	768	25	430	32	63	69	11	100	180	56	0	.	54840	3
	other conifers	230	36	182	42	166	38	291	35	29	71	61	80	560	32	60	45	662	22	303	37	214	55	91	58	194	36	102	.	63	65	155	56	0	.	199	50	65	73	23	80	504	25	976	18	237	30	2641	12	5929	9	486	30	120	65	181	52	62	58	98	72	26	71	30	90	17	100	30	100	4	100	121	75	71	62	277	64	41	58	457	31	30	80	97	58	320	32	89	46	155	34	82	45	205	35	141	52	5	90	52	65	81	41	118	45	271	37	364	30	664	24	408	26	267	33	43	56	97	69	1104	16	262	28	335	36	33	99	30	100	147	49	118	36	27	64	47	80	35	93	165	57	72	82	35	60	27	69	0	.	61	53	0	.	210	51	1	100	53	91	42	100	16	75	5812	9	2680	12	2132	14	29	93	59	59	241	32	127	34	282	32	156	36	246	41	153	49	0	.	34183	3
	beech	1703	17	969	23	642	25	1605	23	283	34	825	29	2540	20	2452	13	3677	11	2845	12	421	38	853	27	1790	21	0	.	571	41	744	30	1071	25	1179	25	0	.	966	22	473	34	488	25	50	64	404	29	0	.	3553	11	1241	23	613	27	1048	24	431	34	277	36	303	33	215	32	360	26	400	30	98	54	998	29	629	28	819	32	881	21	1087	25	1429	20	1738	16	1369	21	959	21	896	24	1518	18	692	25	219	61	727	28	458	29	249	35	749	25	0	.	221	41	0	.	771	27	743	25	554	25	1043	21	774	28	657	26	380	41	11	100	119	52	422	35	132	42	400	32	597	31	55	49	944	26	326	44	124	34	267	28	283	29	995	25	528	31	443	37	234	42	183	44	335	39	9	100	41	72	754	21	319	36	549	32	720	36	808	24	812	22	579	37	355	34	405	30	0	.	67397	2
	other broadleaves	1375	21	716	25	462	22	539	22	152	44	418	30	1442	16	732	20	1732	14	746	19	424	31	433	35	430	24	0	.	238	35	406	42	472	23	678	24	384	42	729	23	216	24	320	33	583	23	1108	17	358	32	1922	14	620	43	413	28	329	23	205	33	117	30	120	46	181	62	80	38	396	37	97	40	305	35	715	24	433	26	501	22	562	28	640	22	509	24	296	29	284	33	321	30	565	24	516	28	170	35	329	33	728	27	711	29	769	35	284	30	736	21	422	30	843	22	1478	20	2306	13	1439	20	795	20	1482	18	204	36	123	46	82	45	362	34	494	37	381	25	398	34	112	40	256	29	632	33	200	43	43	34	344	31	365	34	405	33	233	35	268	45	16	66	361	43	571	24	434	21	1038	15	295	31	428	29	401	27	551	22	472	21	312	32	419	31	453	30	0	.	48364	2
	indeterminable	6	52	0	.	0	.	0	0	0	.	0	100	15	65	1	100	7	100	3	54	2	100	0	.	6	100	0	.	0	0	0	0	0	.	0	0	0	.	7	74	6	100	0	100	1	100	12	76	0	0	1	72	0	0	1	100	0	0	3	100	0	.	0	.	0	.	0	0	0	.	0	0	3	100	0	0	2	73	9	100	0	.	0	0	0	.	0	0	16	87	0	.	2	100	7	100	0	.	0	.	0	.	0	100	0	.	0	0	0	.	0	.	1	100	1	100	6	71	3	79	5	92	0	0	0	0	0	.	0	0	16	69	0	100	2	60	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	100	0	0	0	.	7	100	0	0	1	100	5	89	0	0	0	.	0	.	0	.	6	100	0	0	0	.	0	0	0	.	165	19
	total	4163	14	2775	18	2641	17	4572	14	2609	21	5194	17	20965	7	15873	8	13888	8	9347	9	1244	26	1935	23	3012	18	102	.	1691	24	3802	17	5786	14	4496	16	469	40	5575	13	7218	11	6385	12	7895	10	14417	7	11410	8	12031	8	4593	15	4876	15	6669	13	1505	23	2316	18	1349	25	1281	24	3425	17	1975	20	2305	20	4223	16	3564	17	2905	19	5060	13	3671	18	7187	14	4016	14	3036	17	2450	19	1761	20	3099	16	4174	16	3077	17	3037	19	2706	19	1964	23	2908	18	1444	18	3692	13	2602	16	1888	16	2400	16	2989	12	4247	11	2284	16	3070	13	1708	23	1383	27	1999	21	2508	19	2446	19	4041	17	1907	24	476	37	3312	18	1515	27	1075	25	2557	15	2138	20	2614	18	2585	18	1037	29	1348	27	708	40	1402	27	11152	8	7253	10	10215	9	2219	22	2254	21	2344	20	3208	17	3235	16	1715	23	1591	23	2009	21	0	.	367203	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. 2297016. Birmensdorf, Swiss Federal Research Institute WSL

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