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)
timber assortment classes (HG 2010) · tree species (5 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-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
timber assortment classes (HG 2010)	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	.
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	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	9	39	21	33	25	56	102	51	59	62	50	44	138	19	186	25	169	28	118	26	1	72	5	55	18	89	0	.	34	75	72	50	135	48	73	43	0	.	68	30	34	19	30	22	34	21	77	15	55	16	261	27	57	75	97	41	38	24	78	73	208	35	72	45	61	47	169	31	13	50	18	29	29	38	111	37	17	46	58	46	49	58	43	40	48	56	3	60	22	90	0	90	4	73	20	32	88	50	7	32	37	38	76	52	53	40	5	44	24	45	10	39	0	.	0	100	0	0	2	56	2	67	4	87	5	41	19	67	19	37	12	48	9	37	10	45	19	58	0	68	195	53	14	77	24	56	348	35	75	55	39	52	25	78	5	74	22	73	6	76	45	72	37	18	58	20	91	33	13	76	21	42	18	49	42	46	72	53	29	84	52	69	20	62	0	.	4639	6
	fir	10	66	4	96	7	90	64	51	14	64	54	54	16	29	245	24	143	28	154	34	0	60	2	72	5	43	0	.	4	59	32	89	39	37	85	55	0	.	6	72	2	41	3	52	1	60	6	41	1	100	431	21	14	40	160	31	33	74	63	54	112	32	61	38	60	40	214	36	1	47	4	69	8	44	82	40	0	0	9	39	12	75	44	46	4	66	7	47	5	75	0	56	22	57	6	60	25	57	5	76	33	78	15	52	38	67	0	0	1	75	2	100	0	.	0	.	0	0	3	75	1	75	2	86	8	42	0	.	4	57	16	47	2	62	7	37	4	80	1	100	44	42	43	81	34	53	38	49	50	45	48	41	32	42	6	84	17	68	8	68	10	81	2	81	3	58	11	46	31	79	8	65	24	74	32	42	11	59	1	73	0	0	9	96	0	.	2874	7
	other conifers	3	70	2	72	3	70	4	56	0	.	2	72	24	74	1	57	28	35	19	54	4	100	0	100	2	46	4	.	2	100	16	76	0	.	23	60	0	70	1	72	8	57	9	30	2	51	18	26	53	16	22	49	0	100	25	73	0	0	4	100	1	100	0	100	2	100	1	100	0	.	3	100	2	74	45	97	2	99	2	57	1	83	1	76	2	50	0	75	1	54	2	53	3	55	1	100	0	100	1	100	5	81	8	88	1	57	1	59	5	40	6	42	6	94	0	.	0	97	11	31	3	56	1	73	0	.	0	.	2	63	19	84	0	100	1	100	0	.	3	70	1	58	2	100	5	92	0	.	1	61	0	.	23	96	0	.	0	89	3	100	6	100	68	18	65	30	23	25	0	0	1	100	4	55	5	47	6	51	2	56	1	72	2	87	0	.	637	11
	beech	7	28	2	36	2	63	5	28	1	68	2	37	7	29	10	20	14	15	12	20	1	58	5	35	5	30	0	.	1	70	1	52	6	44	3	42	0	.	4	38	3	45	3	35	0	100	3	56	0	.	11	19	4	39	4	34	5	39	2	59	1	53	1	41	1	38	3	40	2	45	0	71	6	37	2	42	4	40	4	32	3	40	5	37	7	28	5	35	4	33	4	37	7	32	2	38	1	85	2	36	1	51	1	47	2	33	0	.	0	58	0	.	3	41	7	34	7	43	5	30	6	34	4	46	3	47	0	.	1	76	2	50	1	68	2	50	1	58	0	100	5	34	1	83	1	62	1	56	1	49	4	32	1	63	1	47	0	72	1	53	1	71	0	.	0	100	4	42	1	53	2	72	1	48	2	35	3	40	2	44	1	54	1	39	0	.	266	4
	other broadleaves	2	30	2	36	0	59	2	32	0	61	2	46	8	24	3	34	5	22	2	30	2	40	1	74	1	45	0	.	1	60	2	60	2	35	2	37	2	56	3	44	1	39	1	50	3	55	11	27	2	48	6	25	4	57	2	43	3	39	2	60	0	79	0	79	1	97	1	62	3	61	1	60	2	61	2	42	3	46	2	45	3	45	2	43	3	33	1	49	1	37	2	42	3	37	2	45	0	0	1	50	2	41	2	35	3	35	1	84	4	35	3	60	4	40	8	28	18	24	4	40	6	47	13	27	1	41	0	65	0	100	3	42	2	46	1	65	1	54	1	66	0	50	0	100	1	76	0	89	2	43	1	69	1	54	1	71	1	54	0	100	1	71	6	43	2	56	4	30	1	51	1	36	2	39	2	31	3	43	2	47	2	47	1	53	0	.	221	5
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	100	0	100	0	0	0	0	0	100	0	100	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	1	43
	total	30	32	30	27	37	44	177	36	74	61	111	38	192	17	445	21	358	21	304	22	9	49	14	34	30	54	4	.	41	71	122	44	181	39	185	39	3	57	82	26	48	18	46	18	40	19	116	12	110	12	732	17	80	55	286	27	79	39	149	54	323	31	135	38	124	36	387	27	19	37	27	30	46	29	242	35	25	38	74	37	68	56	94	37	63	45	15	27	34	61	9	28	38	38	32	26	115	41	15	31	78	39	102	48	97	40	7	38	35	33	21	28	13	47	15	23	25	21	25	21	17	24	24	24	17	31	20	66	27	31	52	42	13	32	21	33	25	47	6	49	246	43	60	62	65	41	386	32	129	43	92	40	83	42	14	50	40	53	18	59	63	60	112	14	128	19	134	25	46	75	32	41	48	44	84	35	94	45	35	70	56	65	32	53	0	.	8638	4
classes 1 and 2	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	173	35	390	44	185	46	161	39	600	32	790	29	4704	11	1512	17	1537	17	894	19	1	100	102	65	75	45	0	.	285	49	1012	29	608	26	545	32	0	.	933	29	1838	17	1265	21	1635	18	3105	14	2166	15	566	30	437	29	601	31	1555	20	104	60	315	52	76	45	94	48	123	40	641	41	667	32	591	29	312	38	526	43	685	29	300	38	532	25	611	31	251	41	152	46	80	73	72	50	440	29	768	30	294	34	415	44	292	51	584	40	209	42	596	27	498	32	0	.	2	72	1	100	113	59	33	65	84	49	273	41	425	46	681	32	167	44	364	30	411	43	356	47	44	53	267	51	110	65	25	58	491	28	170	66	267	52	365	43	138	74	151	63	15	76	48	79	1383	17	1811	20	1884	17	169	38	294	42	178	42	520	40	568	32	203	50	138	62	296	34	0	.	50344	3
	fir	154	70	27	98	52	65	128	41	107	45	266	35	452	26	1356	16	955	19	818	28	23	70	45	64	142	45	0	.	78	65	50	49	265	50	136	48	0	.	68	49	62	50	73	37	35	79	163	37	17	100	724	22	485	30	600	23	447	24	51	49	52	55	72	50	76	65	150	46	64	49	84	54	263	41	81	39	19	77	134	39	134	62	159	33	47	72	202	42	95	55	45	52	140	37	120	54	142	37	139	44	238	43	156	47	91	93	3	100	45	55	61	100	0	.	0	.	1	100	58	80	47	70	55	77	280	45	0	.	138	51	119	39	94	52	266	41	34	60	18	76	192	38	65	77	70	79	38	75	51	46	122	40	33	58	91	83	95	66	95	80	22	77	53	79	118	50	214	34	104	46	15	60	30	64	143	39	84	45	16	71	20	100	27	59	0	.	13402	5
	other conifers	73	47	34	69	56	88	59	49	0	.	4	100	119	37	39	69	239	33	208	53	1	100	32	61	71	42	24	.	0	0	81	60	0	.	117	60	74	84	5	77	159	36	168	37	107	54	752	22	1950	13	126	52	7	58	68	57	1	100	19	100	21	100	5	100	0	0	31	100	0	.	106	100	26	65	10	83	24	83	55	52	5	86	9	81	203	47	29	69	34	45	61	53	35	63	32	100	7	100	6	100	18	75	48	68	42	46	32	46	223	35	176	39	68	58	0	.	8	58	345	28	115	47	116	69	0	.	0	.	101	56	67	51	6	100	6	100	0	.	101	70	18	59	0	0	5	74	0	.	33	68	0	.	14	74	0	.	5	81	59	100	15	100	1879	15	1026	18	766	21	4	100	27	95	102	44	54	50	85	48	94	51	39	59	28	69	0	.	11014	5
	beech	281	26	173	39	165	39	496	31	62	55	138	44	659	25	854	17	1178	15	1316	17	45	56	288	37	421	35	0	.	102	47	251	34	352	34	270	38	0	.	264	27	167	42	227	35	11	58	186	48	0	.	789	20	277	29	254	33	471	28	89	52	98	35	78	37	74	40	114	35	199	42	20	65	390	35	154	47	335	40	296	35	363	41	400	35	646	26	307	36	365	34	164	35	416	35	133	34	72	47	158	37	183	35	95	52	200	28	0	.	34	66	0	.	339	40	433	34	326	36	495	37	497	35	176	51	278	42	0	.	75	74	119	41	53	53	131	46	121	43	53	80	365	34	35	68	39	58	122	35	80	35	259	34	73	48	137	46	76	59	59	65	68	63	0	.	17	86	503	30	64	50	90	46	80	40	241	30	279	31	116	38	102	36	143	36	0	.	21121	4
	other broadleaves	314	31	221	34	191	39	114	50	7	61	381	37	651	19	435	24	669	20	427	23	161	42	100	44	213	32	0	.	96	47	223	44	379	30	217	36	166	52	338	30	122	32	94	38	434	37	853	26	241	41	697	21	227	31	173	35	243	36	100	46	122	32	59	70	101	68	46	51	293	47	25	63	158	56	260	35	273	39	312	29	331	43	416	28	211	33	92	38	149	37	221	40	202	33	244	39	63	46	134	61	367	32	209	28	193	35	161	43	391	34	192	48	573	32	720	25	1598	19	461	30	416	35	1097	24	215	43	55	78	23	77	263	43	245	48	157	42	201	52	59	48	177	40	21	83	55	49	43	46	276	41	142	50	225	51	202	47	96	47	5	100	47	62	445	39	270	27	483	21	64	55	131	40	178	45	321	29	164	31	239	45	178	46	199	47	0	.	23758	3
	indeterminable	3	100	0	.	0	.	0	.	0	.	1	100	17	67	0	0	0	0	1	100	3	100	0	.	0	100	0	.	0	0	0	0	0	.	0	0	0	.	7	100	0	100	0	0	1	100	0	0	0	0	1	100	0	0	2	100	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	2	100	0	0	0	.	0	0	0	.	0	.	21	84	0	.	0	.	0	0	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	1	100	8	100	0	.	0	0	0	.	0	0	8	80	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	.	85	30
	total	998	22	843	29	649	27	956	22	776	29	1581	23	6603	9	4197	11	4577	11	3663	14	234	38	567	32	922	26	24	.	561	36	1617	23	1604	20	1284	22	240	51	1615	21	2348	15	1827	18	2224	16	5059	11	4374	10	2903	13	1434	20	1698	20	2717	16	362	31	608	32	290	34	345	35	464	26	1197	28	900	29	1428	21	816	26	1179	28	1482	21	1133	28	1517	19	1718	19	881	25	816	27	570	28	866	27	969	23	1052	26	732	26	1221	26	801	28	1110	26	406	31	1289	20	926	24	981	24	1154	22	1938	17	1474	19	1116	23	1527	20	1046	30	480	43	1018	27	742	26	761	28	973	35	712	37	276	47	1019	26	231	50	194	43	694	24	609	31	790	28	711	31	568	37	423	44	233	65	200	48	3760	12	3242	14	3850	13	406	31	558	31	568	28	1279	25	1179	22	669	30	476	33	693	28	0	.	119725	2
classes 3 and 4	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	184	44	97	33	370	45	746	34	1234	30	1380	28	6799	11	2906	14	2407	19	1657	19	13	100	76	58	236	48	0	.	313	47	1035	28	1509	26	735	35	0	.	1638	22	2801	17	1354	22	2711	18	3657	14	2070	15	1217	22	522	29	1019	27	2167	22	81	60	552	27	282	44	233	39	951	31	482	38	495	35	907	29	416	39	520	36	1020	28	986	32	1303	28	693	28	396	49	292	58	160	68	159	55	957	28	896	33	725	33	261	39	364	43	447	34	362	40	785	27	913	30	0	.	21	71	19	100	123	81	25	88	121	59	357	44	588	40	704	32	404	58	651	30	487	34	336	43	93	53	558	31	20	72	170	63	822	23	252	47	358	38	638	38	114	64	183	59	71	87	181	66	1610	17	1537	18	2483	17	263	42	482	37	354	39	473	37	869	29	351	57	328	50	235	37	0	.	72772	3
	fir	158	45	18	83	13	100	778	33	294	39	797	38	739	26	2862	15	1850	19	1318	21	14	100	53	72	122	60	0	.	130	70	151	50	532	37	363	52	0	.	253	57	177	35	193	42	112	69	319	41	0	0	1917	23	581	36	1559	23	740	27	229	49	549	40	255	42	258	42	679	37	150	63	75	49	359	41	475	35	85	63	188	42	248	49	587	30	61	66	121	40	149	65	91	50	148	37	251	55	336	39	251	37	157	53	60	44	138	100	4	100	68	64	86	100	0	.	0	.	17	100	82	72	164	58	64	73	150	45	0	.	144	56	160	44	247	49	421	38	57	61	27	100	531	37	88	77	148	55	144	46	217	49	250	36	212	52	179	67	263	52	86	71	74	71	43	83	194	56	340	43	30	75	72	46	122	57	537	32	225	38	11	100	0	0	88	86	0	.	27488	4
	other conifers	60	53	31	70	66	66	90	55	0	.	16	100	376	61	36	59	442	30	127	49	33	100	6	100	102	44	118	.	15	100	125	71	0	.	160	58	41	71	11	76	199	38	406	29	83	53	895	17	2427	13	179	38	37	78	146	50	0	0	74	100	0	0	0	0	28	100	21	100	0	.	33	100	3	100	112	80	0	0	34	77	11	100	21	100	174	37	48	63	58	53	41	72	80	49	11	100	0	0	35	100	61	66	130	57	91	70	84	50	218	40	137	44	118	64	0	.	37	61	275	32	136	39	165	55	0	.	0	.	45	71	68	55	0	0	0	0	0	.	43	81	4	100	10	100	24	100	0	.	4	100	0	.	55	63	0	.	3	100	16	100	0	0	1935	17	1002	21	903	22	24	100	36	72	122	43	32	49	126	51	88	50	96	67	126	63	0	.	13226	5
	beech	869	28	309	30	237	45	819	31	175	49	323	37	1041	28	1399	18	2132	15	1374	19	240	50	578	39	540	29	0	.	121	71	404	40	548	37	598	36	0	.	503	32	311	51	301	36	21	72	105	35	0	.	1746	18	687	29	338	37	585	32	252	51	92	43	157	37	129	45	249	37	193	57	12	73	612	35	291	40	703	42	425	32	517	40	816	29	953	23	596	32	543	28	519	36	622	28	194	36	150	100	432	36	214	36	126	46	366	33	0	.	67	54	0	.	159	41	355	32	192	36	339	36	178	41	196	48	250	52	0	.	64	59	247	53	91	54	181	56	191	48	16	100	544	30	109	100	62	54	140	44	220	42	622	32	266	60	372	46	29	74	152	52	100	55	0	.	0	0	207	41	140	52	185	44	207	38	337	33	418	28	327	44	230	50	161	36	0	.	32823	3
	other broadleaves	395	32	373	39	125	55	199	40	9	71	150	42	492	27	268	32	798	20	230	30	148	42	83	93	138	43	0	.	106	66	288	46	116	39	187	43	206	66	358	38	79	47	63	69	78	56	274	30	82	56	896	24	485	78	208	52	124	39	68	48	53	53	33	82	31	100	17	58	187	46	12	75	41	60	291	37	162	37	231	35	205	45	349	30	192	40	116	49	66	43	187	51	189	39	243	53	6	100	157	52	324	38	378	33	263	37	4	100	110	41	99	62	73	44	248	28	581	29	135	40	139	42	415	30	77	45	11	100	18	100	171	45	223	66	36	61	129	68	68	63	72	49	104	73	27	71	0	0	125	40	57	43	117	52	87	49	190	58	17	100	137	59	82	49	17	74	214	41	145	53	279	39	119	42	284	28	183	35	137	47	238	43	220	51	0	.	16149	5
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	8	71	0	0	0	0	0	0	0	0	0	.	11	100	0	.	0	0	0	0	0	.	0	0	0	.	0	0	10	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	8	100	0	.	0	.	12	100	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	18	100	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	66	42
	total	1665	22	829	25	810	32	2632	20	1713	28	2665	23	9455	10	7471	11	7629	11	4707	13	448	38	796	35	1149	27	118	.	686	38	2004	23	2705	24	2042	22	247	59	2762	20	3576	15	2317	18	3006	18	5251	12	4579	11	5956	13	2312	24	3269	19	3617	18	705	34	1247	27	728	34	679	34	1917	22	1012	29	626	32	1921	23	1585	27	1471	28	1897	21	1968	26	3077	19	2074	20	1277	27	1117	28	997	30	1198	24	1668	24	1388	28	1599	25	1017	29	1059	30	1305	25	454	33	1248	23	1235	26	349	31	624	26	846	22	953	21	642	28	960	24	834	31	598	40	975	29	1067	31	1212	27	1124	30	713	37	247	45	1708	24	331	57	432	40	1105	21	818	31	1288	26	1288	27	751	40	669	37	342	55	492	45	3670	13	2750	15	4147	14	602	36	1055	30	924	28	1663	24	1822	22	915	32	893	33	832	30	0	.	162523	1
classes 5 and 6	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	11	71	23	100	92	79	285	50	237	46	277	44	1869	15	1297	19	799	26	466	34	0	0	119	100	78	53	0	.	43	71	235	39	569	33	178	46	0	.	793	36	958	26	435	24	1096	21	1221	18	328	31	417	42	356	43	252	44	520	34	40	77	58	70	114	58	45	100	227	39	103	54	94	46	249	40	31	100	181	52	477	32	578	39	936	44	211	38	160	56	10	100	34	77	21	71	554	35	89	61	231	55	32	100	163	60	154	57	271	48	510	45	354	44	0	.	27	73	0	0	57	64	18	100	26	100	162	49	195	51	207	53	143	43	214	37	186	49	88	60	0	0	140	54	0	0	57	71	77	48	126	60	43	71	124	57	0	0	49	80	0	0	93	100	664	30	315	31	714	26	40	71	207	42	62	65	35	61	110	71	89	91	63	58	32	76	0	.	23242	5
	fir	48	68	55	84	47	83	187	40	68	63	152	46	353	36	1056	22	930	25	215	35	0	0	20	100	21	100	0	.	9	100	77	78	167	50	103	62	0	.	109	51	68	51	138	62	68	63	225	42	0	0	252	41	170	57	268	32	245	39	180	60	174	45	41	100	63	70	52	70	47	60	56	93	166	58	177	53	18	100	73	92	83	60	227	47	6	100	85	64	65	72	20	71	18	100	154	55	106	47	102	50	0	0	0	0	108	100	0	0	5	100	5	100	0	.	0	.	11	100	97	64	33	65	18	100	81	66	0	.	0	0	107	54	133	74	193	50	0	0	5	100	28	100	0	0	35	100	75	90	32	100	29	84	32	100	7	100	19	72	112	100	34	100	0	0	74	83	121	46	7	100	59	59	73	66	117	49	36	63	14	100	0	0	38	84	0	.	8702	7
	other conifers	0	0	0	0	0	0	23	71	0	.	0	0	36	81	10	100	166	42	11	100	0	0	0	0	15	100	35	.	0	0	0	0	0	.	7	100	0	0	22	100	40	63	181	35	21	74	338	24	771	21	36	86	35	100	64	91	0	0	10	100	0	0	0	0	0	0	0	0	0	.	0	0	0	0	141	100	0	0	12	100	28	100	6	100	5	100	8	77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	37	71	117	93	31	56	22	58	38	63	0	.	19	100	65	50	27	62	92	66	0	.	0	.	6	100	17	74	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	125	100	0	.	0	0	0	0	0	0	643	23	234	28	200	29	18	100	4	100	0	0	35	100	0	0	0	0	25	100	15	74	0	.	3789	10
	beech	266	40	291	44	170	43	411	43	34	60	34	64	424	47	542	29	712	26	132	35	147	56	92	57	154	49	0	.	7	100	66	60	23	73	114	40	0	.	119	49	81	72	84	51	6	100	43	65	0	.	480	29	242	44	118	50	115	61	91	100	0	0	0	0	0	0	10	100	0	0	0	0	195	45	125	77	23	60	140	55	170	65	171	36	216	45	382	41	48	77	213	44	91	60	32	62	40	100	177	71	95	46	111	60	160	54	0	.	21	60	0	.	76	46	88	54	19	71	79	48	9	100	13	72	64	74	0	.	54	78	108	63	26	61	20	73	168	53	0	0	102	61	27	100	0	0	20	62	26	46	65	83	91	57	33	63	49	71	37	62	67	74	0	.	0	0	73	57	24	100	103	58	73	81	180	47	160	65	173	65	65	61	62	65	0	.	9572	6
	other broadleaves	164	39	79	49	0	0	115	52	0	0	7	100	58	44	114	62	248	57	13	71	124	61	0	0	7	100	0	.	4	100	101	66	23	79	45	49	50	100	43	53	25	79	13	71	6	100	14	71	12	71	87	43	47	59	21	82	0	0	0	0	0	0	0	0	0	0	0	0	39	87	0	0	0	0	98	59	53	71	19	75	39	100	31	78	46	72	7	100	9	100	7	100	36	100	49	68	0	0	23	100	80	91	270	80	79	61	0	0	74	83	30	100	8	100	47	49	128	55	181	79	24	100	279	43	15	79	0	0	0	0	13	100	74	93	0	0	0	0	0	0	0	0	76	100	0	0	0	0	5	100	7	100	9	100	0	0	48	100	0	0	69	71	5	100	0	0	29	62	38	83	166	49	8	100	25	58	54	71	39	100	67	71	39	71	0	.	3859	11
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	489	30	447	37	309	38	1020	26	339	39	470	34	2740	14	3020	15	2855	15	836	24	272	51	231	67	275	35	35	.	63	52	479	33	782	31	447	28	50	100	1085	28	1172	23	852	24	1197	20	1841	15	1111	18	1272	20	850	29	722	27	880	27	321	45	233	44	155	50	108	58	289	34	189	38	150	51	610	29	572	38	274	39	722	28	897	31	1370	33	484	30	642	33	132	51	274	39	165	52	789	31	236	37	533	40	207	45	544	48	537	40	388	44	641	37	410	39	121	37	163	36	176	44	480	35	111	40	429	34	322	36	195	51	267	55	388	36	446	34	398	37	256	48	5	100	270	38	103	78	91	78	172	48	189	54	144	50	380	42	41	55	166	53	148	87	263	50	1312	19	622	24	1137	20	128	53	538	35	216	40	392	36	361	48	315	47	219	41	185	42	0	.	49163	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	377	36	530	36	672	42	1294	32	2130	30	2497	27	13511	10	5901	13	4911	15	3135	17	16	87	301	61	406	46	0	.	674	42	2354	26	2821	24	1531	28	0	.	3432	23	5631	16	3084	19	5475	17	8060	13	4619	14	2461	21	1372	28	1968	25	4281	20	303	49	1134	28	544	40	432	39	1470	28	1239	36	1274	32	1776	26	870	34	1245	36	2240	24	1914	32	2813	29	1564	25	810	43	476	48	274	62	255	52	1972	27	1841	27	1257	32	745	36	895	37	1238	30	848	37	1914	26	1774	30	0	.	50	72	20	100	295	64	78	67	235	54	797	41	1226	40	1612	31	726	48	1238	29	1093	33	800	39	137	50	1160	30	144	58	275	54	1737	21	622	40	707	34	1152	36	256	61	405	55	91	79	367	68	3694	16	3721	17	5172	16	485	39	1004	33	612	36	1070	31	1619	27	673	50	581	46	583	33	0	.	150997	2
	fir	370	46	103	60	119	67	1156	32	484	37	1270	34	1559	25	5519	14	3878	17	2505	20	37	79	121	63	289	52	0	.	221	68	310	58	1002	35	686	47	0	.	435	49	308	35	407	43	217	66	714	37	18	100	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	404	43	478	45	1017	30	118	57	415	35	314	57	156	47	328	31	531	50	610	35	497	35	428	46	231	41	375	58	7	100	119	56	153	100	0	.	0	.	28	100	240	60	245	59	139	69	519	41	0	.	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	389	50	686	35	172	41	153	47	249	46	829	31	356	37	42	82	20	100	163	73	0	.	52466	4
	other conifers	136	43	67	51	124	62	176	46	0	.	22	76	555	51	86	52	874	27	364	46	38	100	38	65	190	45	180	.	17	100	221	63	0	.	307	57	115	73	38	80	406	35	765	29	214	44	2003	17	5201	12	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	102	54	45	89	37	82	384	40	84	66	92	48	104	57	118	48	45	100	7	100	41	100	83	57	186	49	172	59	234	61	477	35	342	38	230	60	0	.	65	69	696	27	281	38	374	55	0	.	0	.	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	4525	15	2326	17	1891	19	46	100	68	80	228	42	126	44	216	41	184	46	161	63	171	61	0	.	28666	5
	beech	1422	26	775	30	574	34	1731	29	272	44	497	33	2131	26	2805	17	4036	14	2835	16	434	43	963	34	1120	28	0	.	231	55	723	36	928	33	985	34	0	.	889	29	562	44	614	31	38	69	338	39	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	864	28	1053	36	1392	28	1822	21	1290	30	961	27	899	33	1137	28	361	32	264	83	769	36	493	31	334	40	727	32	0	.	122	55	0	.	576	36	882	31	543	33	919	31	689	32	389	44	595	43	0	.	195	54	476	43	171	51	334	46	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	.	17	86	787	29	230	47	379	37	361	39	760	31	861	28	618	46	397	42	366	30	0	.	63781	3
	other broadleaves	875	26	674	36	316	35	429	33	16	65	540	38	1209	20	821	28	1719	20	672	21	435	40	184	56	359	32	0	.	208	47	614	46	520	28	451	36	425	59	741	31	227	32	171	43	521	39	1152	25	337	38	1685	21	764	60	404	41	369	30	169	39	175	34	93	54	133	75	64	47	521	46	37	53	201	51	650	33	491	35	564	30	577	41	799	26	451	29	216	35	226	32	417	37	430	31	537	40	69	48	315	49	773	34	859	39	537	30	166	43	579	34	324	55	657	32	1023	24	2324	18	781	31	585	33	1804	24	308	39	66	81	41	87	450	41	543	52	194	39	331	57	128	49	249	36	202	81	83	50	43	46	408	38	208	44	352	49	290	41	335	56	23	82	253	60	538	38	289	28	731	23	248	50	578	35	307	38	633	26	404	27	417	40	485	40	460	40	0	.	43987	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	0	0	0	0	1	100	0	0	2	100	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	2	100	0	0	0	.	0	0	0	.	0	.	29	88	0	.	0	.	12	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	1	100	8	100	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	.	152	28
	total	3183	19	2150	24	1805	26	4785	18	2902	28	4826	22	18989	9	15132	10	15419	10	9511	12	963	35	1608	32	2377	25	180	.	1350	34	4222	22	5272	20	3959	20	540	53	5543	19	7144	14	5042	17	6467	15	12267	10	10175	10	10863	12	4676	20	5976	18	7292	17	1536	31	2410	24	1307	32	1256	32	3057	21	2418	26	1704	30	4005	21	3215	25	2949	25	4175	19	4066	24	6059	19	4339	18	2815	24	2098	25	1849	27	2268	23	3458	23	2792	24	2879	25	2523	25	2506	27	3049	22	1255	29	3212	20	2592	24	1464	23	1955	22	2986	16	2932	18	1886	21	2941	19	2220	28	1293	39	2287	28	2250	26	2433	26	2517	30	1706	35	534	43	3244	21	725	52	782	36	2358	20	1745	27	2314	24	2462	25	1374	35	1297	37	741	54	1018	45	8854	12	6742	13	9268	12	1181	33	2183	27	1757	25	3418	21	3456	21	1935	29	1644	30	1742	26	0	.	340049	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. 2282947. Birmensdorf, Swiss Federal Research Institute WSL

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