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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	60	61	50	44	155	17	186	25	169	28	119	26	1	72	5	55	18	89	0	.	34	75	73	49	141	46	73	43	0	.	73	28	40	17	31	21	51	18	86	14	60	16	261	27	57	75	97	41	38	24	78	73	208	35	72	45	61	47	169	31	13	47	19	29	37	34	111	37	19	43	77	41	49	58	50	36	48	56	3	60	22	90	0	90	4	73	21	31	89	50	8	29	37	38	76	52	53	40	5	42	25	43	10	38	1	100	0	100	0	0	3	44	2	67	7	65	6	38	22	59	20	36	16	41	11	33	14	43	19	58	0	68	196	52	14	77	24	56	348	35	75	54	44	47	25	78	7	61	22	73	6	76	45	72	43	18	59	20	100	31	50	70	21	42	18	49	42	46	72	53	29	84	52	69	20	62	0	.	4835	6
	fir	10	66	4	96	7	90	64	51	14	64	54	54	16	29	245	24	143	28	174	32	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	90	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	47	44	4	66	7	47	5	75	0	56	22	57	6	54	25	57	5	76	33	78	15	52	38	67	0	0	1	75	2	100	0	.	0	.	0	0	3	69	1	75	2	86	8	42	1	100	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	4	53	11	46	31	79	8	65	24	74	32	42	11	59	1	73	0	0	9	96	0	.	2900	7
	other conifers	3	70	2	72	3	70	4	56	0	.	2	72	27	65	1	57	28	35	19	54	4	100	0	100	2	46	4	.	2	100	16	76	0	.	23	60	0	70	1	72	10	48	9	29	3	45	25	22	64	14	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	9	50	1	83	1	76	2	50	0	75	1	54	2	53	3	55	2	65	0	100	1	100	5	81	8	88	1	57	1	46	5	39	8	36	8	77	0	.	0	97	17	25	4	47	1	71	0	.	0	100	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	76	17	72	28	26	23	0	0	1	100	4	55	5	47	6	51	2	56	1	72	2	87	0	.	700	10
	beech	7	28	2	36	2	63	5	27	1	68	2	37	10	36	10	20	14	15	13	19	1	58	5	35	7	31	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	31	3	40	6	32	7	28	5	35	4	33	4	37	7	30	3	35	1	85	2	36	1	51	1	47	2	33	0	.	0	58	0	.	3	39	7	34	7	43	5	30	6	34	4	46	3	47	0	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	.	274	4
	other broadleaves	2	30	2	36	0	59	2	32	0	61	2	46	10	23	3	34	5	22	2	30	2	40	1	74	2	39	0	.	1	60	2	60	2	35	2	37	2	56	3	39	2	40	2	41	3	55	12	27	2	48	6	25	4	57	2	43	3	39	2	60	0	73	0	79	1	97	1	62	3	61	1	60	2	61	2	42	3	45	2	45	3	45	3	39	3	33	1	49	1	37	2	42	3	36	2	42	0	100	1	50	2	41	2	35	3	34	1	78	5	33	3	58	4	38	8	27	18	24	5	35	7	42	13	26	1	41	1	58	0	100	3	42	2	43	1	65	1	54	1	66	1	51	1	78	1	76	0	89	2	43	1	69	1	54	1	71	1	54	0	100	1	71	6	40	3	46	6	28	1	46	1	36	2	39	2	31	3	43	2	47	2	47	1	53	0	.	237	4
	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	.	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	178	36	76	60	111	38	218	16	445	21	358	21	326	22	9	49	14	34	33	50	4	.	41	71	124	44	188	38	185	39	3	57	87	25	57	16	48	18	58	16	132	11	126	11	732	17	80	54	286	27	79	39	149	54	323	31	135	38	124	36	387	27	20	36	27	30	54	27	242	35	27	36	102	32	68	56	106	34	63	45	15	27	34	61	9	28	39	38	35	24	116	41	16	29	78	39	102	48	97	40	7	35	36	31	23	26	16	41	15	23	25	20	35	18	19	22	28	23	19	30	24	55	27	30	57	39	16	29	26	32	25	47	6	49	247	43	60	62	65	41	387	32	129	43	97	38	83	42	16	46	40	53	18	59	63	60	127	13	138	18	146	23	83	58	32	41	48	44	84	35	94	45	35	70	56	65	32	53	0	.	8946	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	164	38	659	30	790	29	5371	10	1512	17	1537	17	895	19	1	100	102	65	79	43	0	.	285	49	1105	28	772	25	545	32	0	.	1011	27	2004	16	1345	20	2061	17	3368	13	2258	14	566	30	486	28	601	31	1658	20	104	60	315	52	76	45	94	48	123	40	676	39	688	31	666	27	312	38	623	39	719	28	300	38	614	23	611	31	251	41	152	46	80	73	74	49	504	27	808	29	374	32	415	44	292	51	584	40	225	39	621	26	506	32	9	100	2	72	1	100	147	47	33	65	171	43	288	39	506	41	698	31	268	38	395	28	463	39	356	47	44	53	299	47	110	65	25	58	513	27	180	62	284	49	365	43	183	61	151	63	15	76	48	79	1504	17	1877	19	2076	16	307	39	294	42	178	42	532	39	568	32	203	50	138	62	296	34	0	.	54255	3
	fir	154	70	27	98	52	65	128	41	107	45	266	35	464	26	1369	15	955	19	825	28	23	70	45	64	144	44	0	.	78	65	50	49	265	50	136	48	0	.	68	49	62	50	73	37	35	79	163	37	24	76	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	136	38	134	62	183	29	47	72	202	42	95	55	45	52	140	37	159	45	142	37	139	44	238	43	156	47	91	93	3	100	45	55	61	100	0	.	0	.	1	100	69	69	47	70	55	77	280	45	34	100	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	121	49	214	34	110	44	15	60	30	64	143	39	84	45	16	71	20	100	27	59	0	.	13560	5
	other conifers	73	47	34	69	56	88	59	49	0	.	4	100	166	33	39	69	239	33	208	53	1	100	32	61	71	42	24	.	0	0	81	60	0	.	117	60	74	84	5	77	186	32	190	34	128	47	1039	18	2325	12	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	183	49	5	86	9	81	203	47	29	69	34	45	61	53	35	63	47	74	7	100	6	100	18	75	48	68	42	46	57	41	230	34	200	35	124	46	0	.	8	58	509	23	132	42	122	66	0	.	11	100	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	2140	14	1106	17	832	20	4	100	27	95	102	44	54	50	85	48	94	51	39	59	28	69	0	.	12655	5
	beech	281	26	173	39	165	39	509	30	62	55	138	44	733	24	854	17	1178	15	1326	17	45	56	288	37	492	31	0	.	102	47	251	34	352	34	270	38	0	.	264	27	174	41	241	34	11	58	225	43	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	347	33	363	41	419	33	646	26	307	36	365	34	164	35	423	35	176	30	72	47	158	37	183	35	95	52	200	28	0	.	34	66	0	.	348	39	433	34	326	36	513	36	515	34	177	51	278	42	1	100	75	74	119	41	53	53	133	45	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	.	23	69	504	30	65	49	90	46	80	40	263	29	279	31	116	38	104	35	143	36	0	.	21551	4
	other broadleaves	314	31	221	34	191	39	117	48	7	61	381	37	855	20	435	24	696	20	432	23	161	42	100	44	315	30	0	.	96	47	226	44	379	30	217	36	186	48	374	28	133	30	185	41	491	33	877	25	272	37	697	21	233	31	173	35	244	36	100	46	122	32	59	70	101	68	46	51	310	45	25	63	191	49	260	35	279	38	334	27	331	43	443	27	211	33	92	38	149	37	221	40	204	33	275	36	170	51	177	52	367	32	209	28	201	34	176	40	449	31	214	43	642	30	823	23	1606	19	689	28	473	31	1163	23	215	43	144	50	28	66	263	43	256	46	203	36	201	52	59	48	203	38	28	68	55	49	46	44	276	41	147	49	225	51	202	47	96	47	5	100	47	62	483	36	361	26	633	19	79	48	144	37	178	45	332	29	164	31	239	45	178	46	199	47	0	.	25808	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	15	100	0	0	1	100	0	0	2	100	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	3	73	0	0	0	.	0	0	0	.	0	.	21	84	0	.	0	.	0	0	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	5	100	1	100	8	100	0	0	0	0	0	.	0	0	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	.	102	29
	total	998	22	843	29	649	27	977	22	836	28	1581	23	7606	9	4210	11	4604	11	3687	14	234	38	567	32	1101	24	24	.	561	36	1714	22	1768	19	1284	22	260	48	1730	20	2558	14	2034	17	2727	15	5687	10	4878	10	2903	13	1488	19	1698	20	2821	16	362	31	608	32	290	34	345	35	464	26	1249	27	922	29	1536	20	816	26	1282	26	1719	20	1133	28	1668	18	1718	19	881	25	816	27	570	28	876	27	1160	22	1199	24	854	25	1221	26	801	28	1118	26	461	28	1379	19	981	23	1124	22	1258	21	1947	17	1928	17	1208	21	1689	19	1061	30	695	34	1040	27	844	24	803	26	1073	32	712	37	276	47	1077	25	237	49	194	43	719	23	620	31	812	28	711	31	613	35	423	44	233	65	200	48	4181	11	3487	13	4260	12	566	30	570	30	568	28	1324	24	1179	22	669	30	479	33	693	28	0	.	127932	1
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	767	33	1331	29	1380	28	7618	11	2906	14	2407	19	1685	19	13	100	76	58	250	46	0	.	313	47	1101	26	1534	26	735	35	0	.	1778	21	2966	16	1469	20	2859	17	3937	13	2080	15	1217	22	586	28	1019	27	2343	22	81	60	552	27	282	44	233	39	951	31	520	36	508	34	943	28	416	39	634	34	1046	27	986	32	1625	24	693	28	396	49	292	58	160	68	174	51	973	28	962	31	729	33	261	39	364	43	447	34	384	38	788	27	923	29	0	0	21	71	19	100	136	73	25	88	144	51	357	44	623	38	709	32	477	50	749	27	546	32	336	43	93	53	558	31	20	72	170	63	890	21	276	44	415	35	638	38	114	64	183	59	71	87	181	66	1632	17	1571	18	2620	16	452	38	482	37	354	39	500	35	869	29	351	57	328	50	235	37	0	.	76488	2
	fir	158	45	18	83	13	100	778	33	294	39	797	38	769	25	2862	15	1850	19	1375	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	6	100	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	196	41	248	49	752	27	61	66	121	40	149	65	91	50	148	37	332	46	336	39	251	37	157	53	60	44	138	100	4	100	68	64	86	100	0	.	0	.	17	100	94	64	164	58	64	73	150	45	0	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	210	53	340	43	91	72	72	46	122	57	537	32	225	38	11	100	0	0	88	86	0	.	27924	4
	other conifers	60	53	31	70	66	66	90	55	0	.	16	100	402	57	36	59	442	30	127	49	33	100	6	100	102	44	118	.	15	100	125	71	0	.	160	58	41	71	11	76	235	34	463	27	91	49	1076	15	2551	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	88	45	11	100	21	100	174	37	48	63	58	53	41	72	80	49	14	83	0	0	35	100	61	66	130	57	91	70	150	40	223	39	174	38	141	54	0	.	37	61	353	27	141	37	182	50	0	.	44	100	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	2171	16	1107	20	918	21	24	100	36	72	122	43	32	49	126	51	88	50	96	67	126	63	0	.	14347	5
	beech	869	28	309	30	237	45	819	31	175	49	323	37	1252	28	1399	18	2132	15	1464	18	240	50	578	39	740	30	0	.	121	71	404	40	548	37	598	36	0	.	503	32	311	51	301	36	21	72	114	33	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	479	30	517	40	957	26	953	23	596	32	543	28	519	36	654	27	235	33	150	100	432	36	214	36	126	46	366	33	0	.	67	54	0	.	169	39	355	32	192	36	339	36	182	40	196	48	250	52	19	100	64	59	247	53	91	54	187	54	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	232	38	155	48	185	44	207	38	337	33	418	28	327	44	234	49	161	36	0	.	33683	3
	other broadleaves	395	32	373	39	125	55	245	38	9	71	150	42	638	29	268	32	798	20	230	30	148	42	83	93	156	39	0	.	106	66	288	46	116	39	187	43	206	66	373	37	83	45	153	51	78	56	296	28	82	56	896	24	485	78	208	52	124	39	68	48	62	48	33	82	31	100	17	58	187	46	12	75	41	60	291	37	166	36	231	35	205	45	453	26	192	40	116	49	66	43	187	51	223	36	243	53	6	100	157	52	324	38	378	33	315	35	4	100	110	41	119	54	75	42	263	27	581	29	169	35	165	39	424	29	77	45	57	84	18	100	171	45	252	59	36	61	129	68	68	63	72	49	158	59	27	71	0	0	125	40	157	65	117	52	87	49	190	58	17	100	137	59	84	48	33	53	231	38	152	51	279	39	119	42	293	27	183	35	137	47	238	43	220	51	0	.	17079	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	.	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	2699	19	1809	27	2665	23	10687	10	7471	11	7629	11	4881	13	448	38	796	35	1381	26	118	.	686	38	2070	23	2730	23	2042	22	247	59	2917	19	3782	14	2579	17	3161	17	5742	11	4720	11	5956	13	2376	23	3269	19	3793	17	705	34	1255	27	728	34	679	34	1917	22	1050	28	640	32	1957	23	1585	27	1588	26	2041	20	1968	26	3809	18	2074	20	1277	27	1117	28	997	30	1278	23	1808	23	1454	26	1604	25	1017	29	1059	30	1357	24	542	30	1256	22	1302	24	385	29	639	25	846	22	1091	19	677	27	1010	23	834	31	742	36	979	29	1141	29	1339	25	1190	29	713	37	247	45	1708	24	385	51	432	40	1173	20	843	30	1444	24	1288	27	751	40	669	37	342	55	492	45	3930	13	2921	15	4341	13	873	33	1055	30	924	28	1700	24	1822	22	915	32	896	33	832	30	0	.	169587	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	263	43	277	44	2126	15	1297	19	799	26	492	33	0	0	119	100	78	53	0	.	43	71	235	39	589	32	178	46	0	.	836	34	1015	25	507	22	1236	20	1414	16	328	31	417	42	356	43	252	44	570	31	40	77	58	70	114	58	45	100	227	39	125	48	104	43	269	38	31	100	242	43	477	32	578	39	1136	38	211	38	160	56	10	100	34	77	27	59	554	35	114	50	231	55	32	100	163	60	154	57	271	48	515	44	365	43	0	0	27	73	0	0	71	55	18	100	49	71	162	49	195	51	207	53	173	40	334	38	237	44	88	60	0	0	140	54	0	0	57	71	87	44	126	60	69	58	124	57	0	0	49	80	0	0	93	100	668	29	315	31	795	24	40	71	207	42	62	65	45	53	110	71	89	91	63	58	32	76	0	.	24856	4
	fir	48	68	55	84	47	83	187	40	68	63	152	46	353	36	1056	22	930	25	243	33	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	400	39	6	100	85	64	65	72	20	71	18	100	163	52	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	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	87	92	59	59	73	66	117	49	36	63	14	100	0	0	38	84	0	.	8992	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	249	37	21	74	392	23	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	169	69	31	56	60	55	62	55	0	.	19	100	87	41	27	62	92	66	0	.	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	680	22	269	28	213	27	18	100	4	100	0	0	35	100	0	0	0	0	25	100	15	74	0	.	4134	9
	beech	266	40	291	44	170	43	411	43	34	60	34	64	436	46	542	29	712	26	182	34	147	56	92	57	475	50	0	.	7	100	66	60	23	73	114	40	0	.	119	49	81	72	84	51	6	100	50	58	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	257	34	216	45	382	41	48	77	213	44	131	51	50	53	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	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	40	73	103	58	73	81	180	47	160	65	173	65	93	52	62	65	0	.	10149	7
	other broadleaves	164	39	79	49	0	0	148	46	0	0	7	100	110	42	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	58	80	6	100	14	71	12	71	87	43	47	59	21	82	0	0	0	0	6	100	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	407	82	0	0	115	64	36	85	8	100	47	49	128	55	181	79	24	100	279	43	15	79	0	0	0	0	13	100	104	72	0	0	0	0	0	0	0	0	128	72	0	0	0	0	5	100	7	100	9	100	0	0	48	100	0	0	69	71	5	100	0	0	61	60	38	83	166	49	8	100	35	50	54	71	39	100	67	71	39	71	0	.	4493	12
	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	.	0	0
	total	489	30	447	37	309	38	1053	25	365	37	470	34	3060	14	3020	15	2855	15	941	22	272	51	231	67	597	41	35	.	63	52	479	33	801	30	447	28	50	100	1128	27	1229	22	1037	23	1338	19	2095	14	1111	18	1272	20	850	29	722	27	930	26	321	45	238	43	155	50	108	58	289	34	211	36	160	48	630	28	572	38	335	35	722	28	897	31	1830	27	484	30	642	33	132	51	274	39	212	46	815	30	260	35	533	40	207	45	544	48	865	45	441	39	687	35	466	35	146	35	163	36	176	44	517	33	111	40	453	33	322	36	195	51	267	55	417	34	596	34	449	35	256	48	5	100	270	38	156	62	91	78	181	46	189	54	170	45	380	42	41	55	166	53	148	87	263	50	1353	19	658	23	1263	19	222	52	538	35	216	40	412	34	361	48	315	47	247	38	185	42	0	.	52624	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	1318	31	2314	29	2497	27	15270	10	5901	13	4911	15	3190	17	16	87	301	61	425	44	0	.	674	42	2515	24	3036	23	1531	28	0	.	3699	21	6025	15	3352	18	6207	16	8806	12	4726	14	2461	21	1485	27	1968	25	4610	20	303	49	1134	28	544	40	432	39	1470	28	1335	33	1319	31	1915	25	870	34	1517	33	2320	23	1914	32	3424	25	1564	25	810	43	476	48	274	62	278	48	2053	26	1973	26	1342	31	745	36	895	37	1238	30	885	36	1949	26	1805	29	10	100	50	72	20	100	357	54	78	67	372	41	813	40	1345	37	1633	30	934	41	1489	26	1260	31	800	39	137	50	1193	29	144	58	275	54	1837	21	657	38	812	32	1152	36	303	54	405	55	91	79	367	68	3847	16	3821	17	5590	15	848	38	1004	33	612	36	1120	30	1619	27	673	50	581	46	583	33	0	.	160435	2
	fir	370	46	103	60	119	67	1156	32	484	37	1270	34	1601	24	5533	14	3878	17	2617	20	37	79	121	63	291	51	0	.	221	68	310	58	1002	35	686	47	0	.	435	49	308	35	407	43	217	66	714	37	31	71	3325	20	1250	31	2586	21	1466	25	524	50	888	35	429	35	457	42	1095	33	263	54	219	49	796	38	815	34	122	62	415	42	478	45	1382	27	118	57	415	35	314	57	156	47	328	31	660	43	610	35	497	35	428	46	231	41	375	58	7	100	119	56	153	100	0	.	0	.	28	100	263	55	245	59	139	69	519	41	34	100	286	53	401	38	475	55	887	39	94	57	52	90	795	33	196	68	286	45	294	47	350	46	449	34	309	47	283	68	395	52	300	63	140	68	98	80	408	47	686	35	319	51	153	47	249	46	829	31	356	37	42	82	20	100	163	73	0	.	53375	4
	other conifers	136	43	67	51	124	62	176	46	0	.	22	76	632	46	86	52	874	27	364	46	38	100	38	65	190	45	180	.	17	100	221	63	0	.	307	57	115	73	38	80	472	31	912	27	243	40	2531	15	5712	11	364	34	79	83	303	52	1	100	107	100	21	100	6	100	29	100	52	100	0	.	141	100	31	66	308	88	25	84	291	43	45	89	37	82	384	40	84	66	92	48	104	57	118	48	63	75	7	100	41	100	83	57	186	49	172	59	377	46	489	35	443	33	336	44	0	.	65	69	967	22	304	36	398	51	0	.	55	100	154	61	171	41	6	100	7	100	0	.	148	70	24	61	12	100	34	90	0	.	38	63	0	.	217	82	0	.	9	87	78	100	21	100	5067	14	2554	16	1989	18	46	100	68	80	228	42	126	44	216	41	184	46	161	63	171	61	0	.	31836	4
	beech	1422	26	775	30	574	34	1744	29	272	44	497	33	2431	25	2805	17	4036	14	2986	15	434	43	963	34	1714	30	0	.	231	55	723	36	928	33	985	34	0	.	889	29	569	44	629	30	38	69	392	37	0	.	3027	16	1211	27	713	31	1175	29	434	51	191	34	236	35	204	41	376	35	395	42	32	58	1202	31	572	41	1065	41	971	27	1053	36	1639	25	1822	21	1290	30	961	27	899	33	1215	27	463	30	264	83	769	36	493	31	334	40	727	32	0	.	122	55	0	.	595	34	882	31	543	33	937	31	712	31	390	44	595	43	20	100	195	54	476	43	171	51	342	45	481	46	69	66	1015	29	172	93	102	51	283	34	328	38	950	32	431	50	544	44	154	54	249	52	236	61	0	.	23	69	813	28	261	43	379	37	361	39	783	30	861	28	618	46	431	40	366	30	0	.	65657	3
	other broadleaves	875	26	674	36	316	35	512	32	16	65	540	38	1613	22	821	28	1747	20	677	21	435	40	184	56	480	29	0	.	208	47	617	46	520	28	451	36	445	56	793	30	243	31	398	42	578	35	1199	24	368	36	1685	21	770	59	404	41	370	30	169	39	189	32	93	54	133	75	64	47	538	44	37	53	234	46	650	33	500	34	586	29	577	41	930	24	451	29	216	35	226	32	417	37	465	30	568	38	176	50	357	44	773	34	859	39	926	45	182	40	679	30	372	49	729	29	1142	23	2333	18	1045	28	669	30	1880	23	308	39	202	51	46	78	450	41	614	48	239	35	331	57	128	49	276	34	315	63	83	50	46	44	408	38	313	44	352	49	290	41	335	56	23	82	253	60	578	36	397	27	930	22	270	47	591	34	307	38	662	26	404	27	417	40	485	40	460	40	0	.	47617	3
	indeterminable	3	100	0	.	0	.	0	.	0	.	1	100	25	68	0	0	0	0	1	100	3	100	0	.	11	100	0	.	0	0	0	0	0	.	0	0	0	.	7	100	10	100	0	0	1	100	15	100	0	0	1	100	0	0	2	100	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	3	73	0	0	0	.	0	0	0	.	0	.	29	88	0	.	0	.	12	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	100	0	.	5	100	1	100	8	100	0	0	0	0	0	.	0	0	26	75	0	0	1	100	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	0	0	0	1	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	169	27
	total	3183	19	2150	24	1805	26	4906	18	3085	27	4826	22	21571	9	15146	10	15446	10	9835	12	963	35	1608	32	3111	24	180	.	1350	34	4387	21	5486	20	3959	20	560	52	5862	18	7627	13	5698	16	7284	14	13656	10	10836	10	10863	12	4794	19	5976	18	7622	16	1536	31	2424	24	1307	32	1256	32	3057	21	2530	25	1749	29	4177	20	3215	25	3232	24	4583	18	4066	24	7413	18	4339	18	2815	24	2098	25	1849	27	2404	23	3819	21	3030	23	3007	24	2523	25	2506	27	3438	22	1451	26	3358	20	2773	23	1671	21	2075	21	2995	16	3570	16	2016	20	3179	18	2236	27	1656	33	2314	27	2458	24	2755	24	2737	28	1706	35	534	43	3303	21	838	47	782	36	2460	20	1780	26	2524	22	2462	25	1420	34	1297	37	741	54	1018	45	9591	11	7204	13	10010	12	1744	32	2195	27	1757	25	3519	21	3456	21	1935	29	1678	30	1742	26	0	.	359089	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2301395. Birmensdorf, Swiss Federal Research Institute WSL

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