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
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	6	34	31	34	21	42	85	40	49	48	75	30	192	15	184	21	182	22	142	27	3	59	7	46	11	75	0	.	35	53	69	44	149	31	126	34	0	0	53	23	68	17	32	17	68	14	96	11	67	12	392	19	42	60	64	36	28	20	49	64	207	27	67	40	75	37	242	24	10	35	21	23	35	24	88	31	40	65	56	31	38	45	76	48	49	41	14	49	34	64	3	54	7	43	18	25	68	38	15	37	66	50	47	51	45	35	6	27	22	28	13	27	1	100	0	98	0	0	4	37	1	64	6	46	6	34	17	47	15	29	15	32	10	25	18	27	19	40	2	58	191	36	18	49	20	42	302	30	63	40	42	35	40	44	4	52	30	63	4	66	42	53	49	13	58	22	114	24	35	59	15	38	16	34	30	39	56	43	20	76	37	55	52	56	0	.	5043	4
	fir	27	74	12	83	7	59	97	46	13	51	74	40	21	33	270	18	125	22	198	21	2	93	8	59	4	34	0	.	14	60	20	85	62	37	88	40	0	.	5	54	5	67	4	32	1	47	5	32	1	90	454	14	13	30	115	27	25	56	62	41	139	28	60	41	49	33	218	25	1	40	3	57	6	37	69	31	6	68	6	34	12	51	49	35	3	57	6	37	16	58	0	48	16	46	7	36	18	50	6	44	35	61	9	50	23	68	0	0	1	46	1	100	0	.	0	.	0	0	3	48	3	43	2	57	5	37	1	75	3	52	14	38	1	55	10	29	11	69	0	100	33	37	39	58	53	34	41	47	41	37	38	34	55	41	4	79	15	50	7	54	13	52	3	46	2	50	16	30	20	70	6	53	14	68	27	36	11	41	1	70	0	0	5	92	0	.	2987	5
	other conifers	3	40	3	49	3	49	5	40	0	89	5	76	24	45	1	50	20	31	12	46	5	60	1	65	3	59	2	.	11	64	11	66	0	.	13	57	0	70	0	72	10	35	14	23	2	38	32	17	153	14	88	43	1	73	15	73	2	59	2	92	1	81	3	97	1	100	0	100	0	100	2	89	1	68	29	90	1	95	8	37	1	76	3	74	1	48	1	50	3	45	2	47	3	43	2	56	0	71	3	87	4	62	5	84	5	43	2	34	9	27	6	33	6	53	1	57	1	84	14	20	3	39	2	45	0	100	0	100	2	48	14	73	0	100	1	88	4	100	2	70	1	51	3	75	5	69	0	.	1	52	0	.	18	71	0	100	0	67	1	100	4	92	77	11	76	30	25	16	0	100	0	71	3	46	3	42	4	42	1	48	4	63	2	59	0	.	816	8
	beech	8	19	2	28	2	37	5	23	1	42	3	36	9	25	9	16	12	13	12	15	2	45	4	28	7	23	0	.	2	38	2	33	5	34	3	31	0	.	4	30	3	36	2	31	0	74	2	46	0	.	14	14	4	29	3	28	4	31	2	40	1	43	1	32	1	30	2	33	2	35	0	62	4	31	2	31	3	34	4	26	3	27	4	26	7	20	5	23	4	24	4	27	8	22	3	30	1	85	2	29	1	42	1	41	2	27	0	.	2	52	0	.	4	28	6	27	6	32	5	23	5	25	4	32	2	41	0	0	1	77	2	39	1	53	2	36	2	37	0	59	7	44	1	41	1	48	1	50	1	40	4	28	2	40	1	38	0	61	1	49	1	44	0	0	0	96	3	29	1	37	2	44	2	36	3	31	3	31	2	36	0	47	1	37	0	.	269	3
	other broadleaves	5	22	3	26	1	32	2	25	1	50	2	36	10	17	3	25	6	18	3	25	2	32	1	52	2	30	0	.	1	44	1	47	2	29	3	27	2	42	4	28	1	38	1	37	3	39	9	21	2	48	8	16	3	42	2	35	2	31	2	41	0	63	0	80	1	58	1	58	2	49	0	47	2	40	3	31	2	33	2	32	3	32	2	36	2	25	1	36	1	32	1	37	3	31	3	36	0	73	1	38	2	34	2	29	2	29	2	43	5	25	3	37	5	29	9	20	18	17	8	23	7	27	11	21	1	37	1	46	1	61	3	44	1	32	2	33	2	34	1	55	1	47	2	43	1	46	0	75	2	32	1	45	2	33	1	49	1	41	0	82	2	43	6	30	3	31	7	21	1	34	1	33	3	30	2	26	3	39	1	37	1	37	1	36	0	.	248	3
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	100	0	100	0	0	0	100	0	84	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	100	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	100	0	.	0	0	0	100	0	.	0	.	0	.	0	100	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	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	100	0	0	0	.	0	100	0	0	0	0	0	100	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	1	31
	total	49	43	52	30	34	31	195	30	64	47	158	29	256	13	467	16	344	17	367	18	15	37	21	32	27	35	2	.	64	40	103	38	218	28	233	28	2	43	66	20	86	15	53	13	74	13	144	9	223	11	955	12	64	41	198	24	62	28	117	41	348	22	132	37	127	28	462	20	17	26	27	22	48	20	192	29	53	58	77	24	56	40	133	38	63	34	28	29	58	41	11	27	36	26	32	18	87	32	28	32	108	38	64	46	77	33	11	22	39	19	23	19	16	25	16	16	24	15	35	13	20	17	25	17	15	24	19	44	21	24	47	30	14	22	32	22	37	42	6	39	234	30	63	41	79	30	344	28	107	32	85	30	117	32	10	39	46	48	13	49	61	43	134	9	140	24	166	18	57	48	26	33	37	32	65	29	77	35	25	60	43	50	62	49	0	.	9364	3
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	143	29	454	30	409	32	212	27	586	24	755	23	5199	8	1544	12	1381	14	723	16	22	64	86	50	104	30	0	.	222	40	954	22	992	18	543	27	1	100	983	20	1976	13	1402	14	2479	12	3598	9	2180	11	649	18	502	21	518	24	1291	16	94	42	206	45	112	40	72	38	188	42	486	31	801	22	784	20	502	26	503	32	929	19	291	30	586	18	478	26	268	31	186	35	131	37	90	29	566	21	778	20	385	26	366	31	261	39	423	37	230	26	607	19	536	24	5	100	16	81	1	100	152	33	21	59	150	34	287	32	420	31	558	25	276	28	390	23	716	22	269	38	47	45	335	30	79	52	57	43	533	20	234	39	235	36	319	30	122	52	149	51	13	53	122	48	1700	12	1748	14	2139	12	332	29	338	38	205	28	371	34	477	27	216	35	152	41	329	30	0	.	54281	2
	fir	159	45	20	80	100	41	122	35	97	36	200	30	406	19	1414	11	1028	15	678	20	28	55	48	46	132	33	0	.	54	58	63	41	294	31	142	33	0	.	97	41	52	42	132	29	45	52	133	28	14	76	753	16	410	23	569	20	502	23	52	43	58	47	68	37	61	50	119	36	55	38	64	45	182	36	124	27	75	47	132	30	128	40	298	27	28	66	165	35	126	44	29	47	194	28	179	30	148	28	128	33	170	36	117	42	51	93	3	79	57	39	34	96	0	.	0	.	1	100	70	42	80	43	77	63	179	39	20	93	97	42	120	32	57	50	280	29	75	35	10	76	122	35	94	53	114	40	59	46	88	46	118	30	30	44	62	69	62	60	86	53	36	60	100	46	83	40	338	24	175	32	30	49	57	58	161	29	128	41	9	71	11	100	20	49	0	.	13584	3
	other conifers	107	32	86	52	79	51	62	31	15	59	11	80	171	26	26	62	208	25	162	41	53	57	33	41	58	34	13	.	6	100	49	59	0	.	77	53	42	84	3	78	224	28	324	23	113	36	1041	13	2324	9	163	44	16	56	42	53	41	65	18	71	11	100	21	87	0	0	18	100	4	100	77	84	44	63	24	48	24	60	246	31	6	67	38	71	149	37	19	61	71	39	42	44	104	47	87	51	4	93	9	72	24	43	38	56	130	49	101	28	292	27	165	28	127	33	30	56	46	91	453	17	134	34	111	43	16	97	6	100	89	45	55	44	10	69	15	60	3	100	63	63	36	74	9	71	4	61	0	.	37	54	0	.	51	38	1	100	10	74	32	100	12	71	2345	9	1155	13	913	14	4	72	25	64	108	34	51	37	84	36	71	41	53	43	19	62	0	.	13490	3
	beech	345	18	221	26	159	27	519	26	57	43	203	31	786	19	838	13	977	12	1201	13	40	42	217	29	444	23	0	.	146	31	207	28	288	27	322	28	0	.	307	24	171	29	216	25	15	64	206	34	0	.	1032	15	300	25	215	29	375	23	86	36	123	38	82	29	69	31	138	30	180	35	43	49	295	29	132	34	261	33	308	24	342	30	405	26	579	19	236	28	344	26	199	29	405	24	247	26	73	43	143	31	155	27	72	42	233	25	0	.	63	39	0	.	347	27	353	27	288	27	582	22	461	27	332	30	175	38	1	100	46	72	137	38	45	42	114	35	122	29	31	77	413	32	78	42	50	43	144	36	86	30	251	26	176	31	93	41	60	49	50	48	78	42	7	100	32	66	426	23	76	33	115	36	95	28	231	26	241	23	137	30	88	29	112	30	0	.	21089	3
	other broadleaves	577	27	250	24	238	23	212	26	102	50	256	32	802	14	409	18	718	15	431	18	137	32	191	33	272	23	0	.	117	38	163	39	305	24	317	27	148	38	333	22	137	25	138	32	499	22	814	17	278	34	864	14	220	25	201	28	230	26	112	33	73	30	77	50	62	60	46	39	204	41	62	38	259	39	326	25	232	29	276	22	278	32	325	23	198	25	136	34	147	28	167	33	239	26	313	26	145	37	166	37	368	28	212	25	189	27	242	30	447	21	281	26	623	21	816	18	1571	14	821	19	575	21	941	18	139	41	89	45	62	43	224	35	265	32	308	26	236	35	60	39	168	31	113	35	156	44	34	36	198	35	127	34	253	34	125	42	70	40	4	77	101	46	452	24	395	21	720	14	86	34	101	32	209	31	276	23	180	25	195	35	152	37	147	38	0	.	25934	2
	indeterminable	6	52	0	.	0	.	0	0	0	.	0	100	11	63	1	100	7	100	3	54	2	100	0	.	0	100	0	.	0	0	0	0	0	.	0	0	0	.	7	74	0	100	0	100	1	100	12	76	0	0	1	72	0	0	1	100	0	0	3	100	0	.	0	.	0	.	0	0	0	.	0	0	3	100	0	0	2	73	9	100	0	.	0	0	0	.	0	0	12	83	0	.	2	100	0	0	0	.	0	.	0	.	0	100	0	.	0	0	0	.	0	.	1	100	1	100	6	71	3	79	5	92	0	0	0	0	0	.	0	0	6	68	0	100	2	60	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	100	0	0	0	.	0	0	0	0	1	100	2	75	0	0	0	.	0	.	0	.	1	100	0	0	0	.	0	0	0	.	110	20
	total	1338	16	1032	19	986	19	1128	17	857	21	1426	18	7374	7	4231	8	4319	8	3199	11	280	28	574	23	1010	18	13	.	544	25	1435	18	1879	14	1401	18	191	39	1730	15	2560	11	2212	12	3152	10	5803	7	4796	7	3463	9	1446	16	1547	16	2440	13	366	23	470	25	360	28	264	27	508	22	928	22	1048	21	1567	16	1108	19	1096	21	1900	14	1045	21	1652	16	1432	15	824	19	886	20	568	21	1034	18	1391	16	1148	17	830	20	1084	20	700	22	1025	20	575	20	1467	14	1016	16	1103	16	1216	15	1912	13	2080	12	1276	16	1611	14	796	25	535	27	852	21	818	18	767	21	1435	19	704	25	211	38	1074	20	374	30	380	30	770	18	643	24	731	20	829	20	404	31	350	36	184	48	349	30	4605	8	3414	10	4538	9	673	21	609	25	673	20	1090	19	1110	17	628	23	456	24	627	22	0	.	128488	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	171	34	213	36	537	28	654	26	892	26	1471	21	7037	8	3354	12	2098	15	1417	15	48	49	95	42	180	37	0	.	309	38	1018	22	1606	18	667	27	6	100	1571	16	2739	13	1746	15	2955	12	4323	9	2356	12	1439	16	645	22	725	24	1901	17	157	45	529	20	279	38	229	33	920	22	383	30	738	23	1018	20	547	26	546	29	1264	19	765	26	1624	19	655	23	392	33	287	40	205	40	211	32	1006	21	1100	21	772	24	514	39	292	38	336	33	315	28	718	20	767	22	0	0	64	73	10	100	181	41	14	87	143	38	333	32	514	31	601	25	591	32	669	21	1120	21	274	35	59	48	800	28	52	45	156	42	985	17	515	32	452	26	647	26	64	64	252	46	92	48	261	43	1895	12	1622	15	2480	12	602	34	537	32	365	29	433	27	599	26	293	44	295	39	374	36	0	.	77083	2
	fir	260	41	91	73	116	44	564	27	287	31	662	29	857	20	3328	11	1621	15	1514	21	53	83	174	60	106	45	0	.	75	71	101	46	577	25	526	33	0	.	238	39	164	39	298	27	104	49	269	30	4	100	1735	16	515	28	1289	20	748	22	184	39	572	29	224	38	248	35	788	25	110	52	76	35	263	35	444	26	185	37	226	33	301	35	864	23	58	51	171	35	318	40	59	46	208	31	338	36	285	33	206	30	242	42	59	43	84	100	2	100	115	54	50	92	0	.	0	.	9	100	118	41	159	40	83	53	118	40	0	0	86	52	241	40	153	45	431	25	71	48	15	100	338	34	203	51	243	36	183	42	221	42	210	30	228	41	100	67	163	48	148	58	89	47	144	55	171	41	321	29	211	33	101	62	151	45	451	26	229	32	21	76	0	0	100	60	0	.	28469	3
	other conifers	108	44	64	36	79	42	162	38	14	100	45	82	319	43	27	48	321	26	116	47	146	54	51	74	124	40	67	.	25	69	96	60	0	.	96	54	23	71	6	76	241	27	467	19	98	37	1200	12	2673	11	186	30	57	69	86	51	19	64	72	71	4	100	7	100	16	100	12	100	0	0	37	67	26	68	126	56	11	72	186	36	7	100	51	69	139	30	44	46	81	36	39	56	93	35	48	59	0	0	41	71	46	55	75	56	116	39	140	30	297	25	187	29	100	43	12	58	40	59	458	20	110	30	161	36	15	100	24	100	48	57	41	51	10	73	28	100	29	100	80	66	34	94	22	59	19	79	0	.	23	65	0	.	72	43	0	0	41	95	9	100	0	0	2559	11	1134	14	987	16	14	100	31	58	128	37	47	41	174	37	84	39	160	43	112	50	0	.	15619	4
	beech	962	19	447	26	288	31	767	23	188	35	478	32	1233	21	1225	15	1918	12	1463	14	226	39	501	30	924	21	0	.	313	44	438	33	635	26	599	26	0	.	523	23	236	41	221	31	25	63	132	30	0	.	2083	12	686	23	257	32	509	25	243	36	145	39	188	33	138	37	214	28	201	38	54	62	501	31	312	29	517	34	450	24	561	27	824	20	974	17	645	23	568	22	475	27	874	19	371	29	119	75	358	29	203	32	109	39	387	27	0	.	117	41	0	.	266	27	315	25	232	27	374	24	263	36	258	30	165	48	11	100	42	55	213	41	70	44	259	35	285	33	24	53	460	26	213	47	73	38	108	35	181	32	640	26	272	40	328	38	66	49	96	47	166	40	3	100	8	100	267	25	175	41	220	36	298	30	379	26	408	23	313	39	162	44	173	31	0	.	34610	3
	other broadleaves	599	26	320	29	213	30	219	29	46	67	151	32	526	21	256	24	743	16	289	25	178	35	172	43	131	32	0	.	113	45	184	43	132	36	275	27	202	46	342	28	63	37	149	40	75	37	255	23	68	44	953	17	350	61	191	37	89	34	86	41	40	43	40	55	36	60	33	47	154	38	34	74	41	41	293	27	149	29	212	27	235	30	293	25	197	28	149	36	122	57	133	43	288	28	172	46	25	87	124	41	298	30	317	27	313	29	35	44	144	27	111	41	174	34	410	24	545	20	293	23	186	27	342	23	55	41	34	77	19	64	125	39	166	54	72	32	149	40	51	53	78	42	306	35	43	53	9	74	120	35	230	44	135	36	96	39	164	45	12	80	190	43	104	35	35	52	242	24	173	33	228	32	141	35	241	24	213	25	94	40	206	35	208	38	0	.	17249	3
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	5	71	0	0	0	0	0	0	0	0	0	.	6	100	0	.	0	0	0	0	0	.	0	0	0	.	0	0	6	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	4	100	0	.	0	0	7	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	10	100	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	7	100	0	0	0	0	3	100	0	0	0	.	0	.	0	.	5	100	0	0	0	.	0	0	0	.	54	34
	total	2100	15	1135	20	1233	19	2366	15	1426	22	2807	18	9977	7	8191	9	6701	9	4799	11	651	27	992	25	1470	18	67	.	835	28	1837	19	2951	16	2164	18	231	43	2681	14	3449	12	2881	13	3258	12	6178	8	5101	10	6395	9	2252	17	2549	16	3266	14	742	24	1291	20	738	28	666	26	1967	17	848	23	939	22	1850	17	1722	19	1408	21	2338	15	1869	19	3656	14	2023	15	1400	19	1381	21	911	23	1673	16	1942	17	1529	20	1500	20	1303	23	851	25	1235	21	492	21	1392	16	1114	18	539	19	801	18	837	16	1423	13	732	21	987	16	685	25	582	29	797	22	1221	22	1069	20	1909	18	809	25	229	39	1711	20	796	28	534	28	1284	17	1061	23	1531	19	1354	19	589	33	686	30	357	41	713	28	4704	9	2971	11	4301	10	1176	24	1118	23	1082	21	1551	19	1627	17	805	26	824	27	967	24	0	.	173084	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	15	51	37	52	124	48	180	48	162	41	401	32	2129	11	1292	15	702	20	447	27	27	82	79	86	43	52	0	.	104	52	225	31	413	27	193	35	13	100	737	25	960	20	769	26	1318	15	1674	15	502	25	404	29	295	34	149	43	470	23	54	67	51	58	64	58	63	54	269	32	101	44	242	30	433	31	56	47	192	37	531	23	385	34	1059	27	169	34	153	42	10	72	19	77	56	50	507	28	192	36	300	38	35	71	101	59	93	58	241	35	466	30	369	29	0	0	56	75	0	0	68	41	10	100	87	65	113	42	247	45	268	43	239	35	425	31	470	30	82	44	6	100	155	42	19	100	52	55	83	34	256	35	105	36	85	46	0	0	87	48	28	61	94	68	816	21	352	23	788	22	112	39	156	35	75	45	119	46	102	56	77	69	77	59	63	47	0	.	25848	3
	fir	68	45	49	66	56	56	222	48	60	51	251	36	566	29	1241	16	672	21	332	31	0	0	63	72	12	100	0	.	5	100	46	79	150	39	155	45	0	.	166	50	56	48	217	34	54	53	155	38	0	0	244	31	191	41	238	27	264	29	116	54	135	38	22	100	72	50	213	41	28	60	45	73	124	48	113	48	62	67	67	65	73	46	464	28	9	73	113	47	60	48	14	60	30	62	198	46	95	39	117	43	11	100	0	0	65	100	0	0	84	64	2	100	0	.	0	.	6	100	61	56	159	53	47	81	49	66	0	0	24	100	93	46	88	63	167	37	75	64	3	100	66	58	18	73	30	74	62	65	33	71	56	45	38	71	4	100	35	72	89	75	26	82	54	62	62	59	89	38	89	60	33	59	100	45	128	38	61	46	32	79	0	0	56	61	0	.	9799	5
	other conifers	12	100	29	71	6	71	62	48	0	0	0	0	47	49	6	100	114	37	13	71	10	100	6	100	8	100	20	.	21	100	0	0	0	.	12	76	0	0	13	100	29	50	172	31	23	51	368	18	779	16	49	69	46	71	39	91	0	0	6	100	10	100	0	0	0	0	0	0	0	0	4	100	0	0	98	88	5	100	17	60	16	100	6	71	31	72	24	63	0	0	0	0	6	100	5	100	0	0	0	0	6	100	0	0	21	71	121	55	65	34	50	47	34	55	0	0	10	100	179	30	15	63	61	55	3	100	0	0	8	71	9	75	7	100	3	100	0	0	20	100	0	0	0	0	0	0	0	.	0	0	0	.	68	100	0	0	3	100	0	0	0	0	831	14	314	21	207	23	11	100	2	100	3	100	26	79	20	50	0	0	28	64	20	65	0	.	4258	6
	beech	388	25	299	32	194	30	314	36	37	49	142	39	512	32	380	25	770	20	168	26	153	52	131	41	415	36	0	.	110	59	97	41	144	38	254	44	0	.	132	42	63	58	49	52	9	74	64	46	0	.	424	22	252	40	137	45	159	44	100	57	8	100	32	65	7	100	6	100	18	71	0	0	197	50	183	41	38	46	120	43	180	41	196	29	178	34	483	30	42	55	219	33	231	35	72	37	26	90	223	47	99	50	68	60	126	44	0	.	39	61	0	.	154	75	69	45	28	46	83	36	45	51	63	53	39	74	0	0	29	78	71	59	15	61	25	54	188	39	0	0	64	55	34	61	0	0	14	53	15	46	100	46	79	43	20	63	108	45	37	56	91	50	0	0	0	0	57	45	67	56	212	42	327	53	195	34	161	42	127	55	105	51	119	46	0	.	11429	5
	other broadleaves	194	33	142	41	9	100	106	40	3	100	9	71	105	43	65	61	265	36	22	53	108	46	69	73	26	87	0	.	7	72	58	65	32	51	83	59	32	88	50	41	15	79	31	80	7	71	30	59	9	59	97	29	47	49	18	61	8	100	5	100	3	100	3	100	82	100	0	0	36	61	0	0	3	100	92	44	49	55	10	75	46	61	20	69	112	56	11	72	14	73	19	83	35	67	29	69	0	0	38	74	59	77	181	68	266	76	5	100	140	42	27	67	42	59	243	72	171	36	318	43	27	61	188	36	9	79	0	0	0	0	10	76	61	73	0	0	11	71	0	0	8	100	211	46	0	0	0	0	23	89	7	71	14	59	11	100	33	88	0	0	69	57	9	76	0	0	69	52	34	62	98	48	49	65	32	49	76	40	22	100	59	58	96	43	0	.	4933	9
	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	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	676	20	557	24	388	24	884	22	262	32	803	27	3359	11	2984	11	2524	12	982	18	298	37	348	36	505	31	20	.	248	38	427	27	738	22	698	28	45	69	1098	20	1123	18	1239	21	1411	15	2291	12	1291	15	1219	15	832	23	581	24	901	19	281	35	208	36	120	40	224	44	487	29	182	31	291	29	758	24	543	27	347	32	745	20	701	25	1746	21	499	24	784	23	125	34	272	29	357	30	809	24	313	27	679	30	211	36	349	43	571	42	367	29	793	21	449	25	230	52	368	50	215	30	709	22	257	36	446	25	212	33	247	45	329	39	423	28	596	27	665	25	356	35	30	72	293	32	282	39	82	54	159	34	327	31	267	27	285	32	34	49	265	35	154	56	279	37	1710	14	729	17	1211	17	313	30	501	28	553	34	502	25	420	31	257	38	268	35	354	30	0	.	56268	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	336	29	735	28	1091	27	1131	25	1689	24	2702	20	14558	8	6375	11	4364	12	2728	14	101	48	266	43	338	33	0	.	671	33	2266	20	3160	17	1530	21	20	100	3344	16	5742	12	3949	14	6819	11	9691	9	5104	11	2883	14	1484	21	1456	21	3691	16	354	36	993	21	522	35	438	31	1618	21	980	28	1802	21	2271	18	1193	23	1282	27	2781	17	1479	25	3345	19	1352	20	827	29	516	34	359	35	362	30	2097	20	2138	19	1473	24	981	31	701	32	896	27	791	25	1813	19	1685	21	6	100	135	74	11	100	406	35	46	63	386	32	739	30	1199	29	1442	24	1121	29	1494	21	2324	21	644	30	114	40	1481	24	168	39	285	35	1902	16	1067	29	833	23	1092	25	191	48	517	39	137	44	519	38	4459	12	3780	13	5521	11	1081	29	1047	28	661	27	954	24	1234	23	606	37	561	33	818	29	0	.	162254	2
	fir	513	37	173	53	279	38	1005	27	456	30	1187	27	1849	20	6252	11	3446	13	2723	17	83	72	293	57	254	38	0	.	148	60	231	48	1084	23	910	31	0	.	506	35	277	37	651	27	205	46	562	28	19	71	3186	14	1129	24	2212	17	1539	21	414	39	903	25	374	31	430	35	1337	24	194	45	188	37	575	33	750	25	329	38	431	32	513	32	1675	22	98	47	456	30	520	38	102	42	447	26	720	33	545	29	457	28	458	36	185	39	223	59	5	88	257	50	87	94	0	.	0	.	15	100	252	37	402	38	210	50	351	36	20	93	209	44	469	35	299	49	887	25	232	39	29	90	559	28	355	46	440	32	345	37	384	34	422	27	350	38	170	64	275	44	330	49	164	47	301	50	318	37	764	23	495	29	171	50	321	42	768	25	430	32	63	69	11	100	180	56	0	.	54840	3
	other conifers	230	36	182	42	166	38	291	35	29	71	61	80	560	32	60	45	662	22	303	37	214	55	91	58	194	36	102	.	63	65	155	56	0	.	199	50	65	73	23	80	504	25	976	18	237	30	2641	12	5929	9	486	30	120	65	181	52	62	58	98	72	26	71	30	90	17	100	30	100	4	100	121	75	71	62	277	64	41	58	457	31	30	80	97	58	320	32	89	46	155	34	82	45	205	35	141	52	5	90	52	65	81	41	118	45	271	37	364	30	664	24	408	26	267	33	43	56	97	69	1104	16	262	28	335	36	33	99	30	100	147	49	118	36	27	64	47	80	35	93	165	57	72	82	35	60	27	69	0	.	61	53	0	.	210	51	1	100	53	91	42	100	16	75	5812	9	2680	12	2132	14	29	93	59	59	241	32	127	34	282	32	156	36	246	41	153	49	0	.	34183	3
	beech	1703	17	969	23	642	25	1605	23	283	34	825	29	2540	20	2452	13	3677	11	2845	12	421	38	853	27	1790	21	0	.	571	41	744	30	1071	25	1179	25	0	.	966	22	473	34	488	25	50	64	404	29	0	.	3553	11	1241	23	613	27	1048	24	431	34	277	36	303	33	215	32	360	26	400	30	98	54	998	29	629	28	819	32	881	21	1087	25	1429	20	1738	16	1369	21	959	21	896	24	1518	18	692	25	219	61	727	28	458	29	249	35	749	25	0	.	221	41	0	.	771	27	743	25	554	25	1043	21	774	28	657	26	380	41	11	100	119	52	422	35	132	42	400	32	597	31	55	49	944	26	326	44	124	34	267	28	283	29	995	25	528	31	443	37	234	42	183	44	335	39	9	100	41	72	754	21	319	36	549	32	720	36	808	24	812	22	579	37	355	34	405	30	0	.	67397	2
	other broadleaves	1375	21	716	25	462	22	539	22	152	44	418	30	1442	16	732	20	1732	14	746	19	424	31	433	35	430	24	0	.	238	35	406	42	472	23	678	24	384	42	729	23	216	24	320	33	583	23	1108	17	358	32	1922	14	620	43	413	28	329	23	205	33	117	30	120	46	181	62	80	38	396	37	97	40	305	35	715	24	433	26	501	22	562	28	640	22	509	24	296	29	284	33	321	30	565	24	516	28	170	35	329	33	728	27	711	29	769	35	284	30	736	21	422	30	843	22	1478	20	2306	13	1439	20	795	20	1482	18	204	36	123	46	82	45	362	34	494	37	381	25	398	34	112	40	256	29	632	33	200	43	43	34	344	31	365	34	405	33	233	35	268	45	16	66	361	43	571	24	434	21	1038	15	295	31	428	29	401	27	551	22	472	21	312	32	419	31	453	30	0	.	48364	2
	indeterminable	6	52	0	.	0	.	0	0	0	.	0	100	15	65	1	100	7	100	3	54	2	100	0	.	6	100	0	.	0	0	0	0	0	.	0	0	0	.	7	74	6	100	0	100	1	100	12	76	0	0	1	72	0	0	1	100	0	0	3	100	0	.	0	.	0	.	0	0	0	.	0	0	3	100	0	0	2	73	9	100	0	.	0	0	0	.	0	0	16	87	0	.	2	100	7	100	0	.	0	.	0	.	0	100	0	.	0	0	0	.	0	.	1	100	1	100	6	71	3	79	5	92	0	0	0	0	0	.	0	0	16	69	0	100	2	60	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	100	0	0	0	.	7	100	0	0	1	100	5	89	0	0	0	.	0	.	0	.	6	100	0	0	0	.	0	0	0	.	165	19
	total	4163	14	2775	18	2641	17	4572	14	2609	21	5194	17	20965	7	15873	8	13888	8	9347	9	1244	26	1935	23	3012	18	102	.	1691	24	3802	17	5786	14	4496	16	469	40	5575	13	7218	11	6385	12	7895	10	14417	7	11410	8	12031	8	4593	15	4876	15	6669	13	1505	23	2316	18	1349	25	1281	24	3425	17	1975	20	2305	20	4223	16	3564	17	2905	19	5060	13	3671	18	7187	14	4016	14	3036	17	2450	19	1761	20	3099	16	4174	16	3077	17	3037	19	2706	19	1964	23	2908	18	1444	18	3692	13	2602	16	1888	16	2400	16	2989	12	4247	11	2284	16	3070	13	1708	23	1383	27	1999	21	2508	19	2446	19	4041	17	1907	24	476	37	3312	18	1515	27	1075	25	2557	15	2138	20	2614	18	2585	18	1037	29	1348	27	708	40	1402	27	11152	8	7253	10	10215	9	2219	22	2254	21	2344	20	3208	17	3235	16	1715	23	1591	23	2009	21	0	.	367203	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2282517. Birmensdorf, Swiss Federal Research Institute WSL

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