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
number of stems
tree age (in 40-year classes) · conifers and broadleaves
regional demarcation: forest district (2024)
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
tree age (in 40-year classes)	conifers and broadleaves	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	99	0	.	2	70	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	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	65
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	97	0	.	0	.	0	.	0	.	0	.	0	.	8	98	0	.	0	.	2	98	3	98	3	98	0	.	0	.	0	.	0	.	0	.	0	.	7	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	38
	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	.	4	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	99	0	.	3	56	0	100	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	97	0	.	0	.	0	.	0	.	0	.	0	.	8	98	0	.	0	.	4	68	3	98	3	98	0	.	0	.	0	.	0	.	0	.	0	.	7	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	37
=40 years	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	.
	conifers	20	35	91	38	108	53	36	47	11	51	95	53	28	21	61	21	122	18	41	24	5	96	43	74	23	48	0	.	59	74	61	39	73	38	70	66	10	90	39	36	54	23	49	34	40	28	65	16	25	32	57	20	108	45	139	23	71	37	67	63	75	48	35	49	39	41	59	26	12	57	36	47	59	63	62	51	139	53	43	43	17	40	23	35	108	35	92	45	5	67	4	73	49	58	52	51	92	43	16	46	90	44	137	43	64	58	28	40	48	47	6	54	0	.	0	.	1	99	18	57	10	98	9	55	78	48	31	64	41	43	83	45	36	36	77	69	161	50	153	39	79	47	159	46	56	62	17	36	20	72	13	49	25	51	108	96	87	55	0	.	142	86	45	35	48	22	23	29	106	50	125	70	65	58	100	45	58	68	50	72	84	82	38	69	0	.	54	5
	broadleaves	87	29	78	26	138	42	41	34	13	56	46	34	30	20	50	18	61	15	51	19	95	43	100	33	69	33	0	.	65	57	53	40	65	28	51	33	19	90	98	28	28	40	43	30	97	37	57	32	6	35	56	25	51	27	29	26	39	35	95	56	70	39	85	56	101	29	25	43	115	49	11	62	27	60	87	28	122	35	88	34	122	38	52	27	62	52	128	28	53	63	84	46	53	41	47	54	25	39	82	69	126	30	84	36	81	30	164	57	93	40	45	53	139	33	96	24	126	35	125	28	121	24	141	32	80	41	116	66	27	58	121	33	82	33	94	56	107	39	165	43	48	31	69	43	53	54	3	68	14	47	58	48	43	51	27	69	72	54	13	85	53	44	28	30	52	28	45	32	56	50	48	35	88	31	60	28	65	32	125	38	47	53	150	37	0	.	62	4
	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	107	27	169	27	246	31	77	33	23	39	141	38	58	15	111	15	183	14	91	16	99	41	143	40	92	26	0	.	125	56	114	29	138	24	120	42	29	90	137	24	82	20	93	22	137	27	122	17	31	27	112	16	158	33	168	20	111	29	162	40	145	29	120	44	141	24	84	23	126	44	48	46	86	48	149	35	261	40	131	28	139	36	74	26	171	29	220	26	58	58	88	44	102	34	98	44	118	33	98	58	215	26	221	32	145	33	192	50	141	31	52	47	139	33	96	24	127	35	143	25	132	23	150	30	159	38	147	63	68	36	204	26	118	30	171	42	268	35	319	32	126	36	228	44	109	49	20	37	34	45	71	42	68	36	135	83	159	40	13	85	195	59	73	26	100	20	68	28	162	35	173	49	153	32	160	29	124	34	175	31	131	53	188	35	0	.	117	3
41-80 years	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	.
	conifers	62	40	39	84	58	36	12	37	137	46	83	42	120	12	101	11	49	20	121	17	11	54	49	52	58	34	150	.	111	40	152	33	74	35	107	37	51	90	94	28	118	22	133	20	96	33	134	16	132	11	68	19	110	28	90	24	151	15	99	55	134	28	89	28	97	37	116	26	268	47	241	28	108	36	61	33	66	64	95	25	65	32	62	24	39	37	52	45	38	37	34	52	30	34	68	23	85	40	78	31	131	41	98	48	122	45	86	38	111	27	131	22	11	67	0	.	2	74	92	31	35	47	40	57	182	32	71	56	237	24	51	33	67	34	135	52	49	40	18	62	86	41	53	52	51	56	157	22	141	52	168	49	69	36	78	66	76	45	184	77	47	51	147	16	205	18	130	18	83	49	29	76	52	39	50	49	84	28	22	58	43	77	90	35	0	.	98	3
	broadleaves	70	22	62	30	57	35	98	29	33	53	74	37	54	20	59	15	65	15	166	15	95	45	116	24	176	28	0	.	105	38	106	34	123	28	63	37	232	27	62	23	21	41	28	43	28	57	57	27	26	50	96	18	98	24	75	33	109	22	93	49	62	26	35	30	54	50	48	26	132	32	24	44	88	36	78	30	76	39	91	32	208	34	106	20	111	25	62	42	101	30	83	29	171	22	126	29	23	37	55	45	94	27	57	25	51	24	60	48	67	44	67	51	350	23	342	19	496	13	194	27	261	25	316	21	186	30	3	94	41	61	84	34	104	45	75	33	118	37	31	46	72	24	17	62	136	66	64	32	190	33	97	25	83	29	285	34	75	50	53	85	106	48	29	36	29	40	115	19	35	53	95	32	66	41	77	30	48	26	47	55	114	37	41	31	0	.	91	4
	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	132	23	101	37	115	26	111	27	170	39	158	28	173	11	160	9	114	11	288	12	106	40	165	22	233	21	150	.	217	24	258	22	198	20	170	25	283	30	155	20	139	19	161	17	124	28	192	13	159	12	164	13	208	20	165	22	260	13	192	33	196	19	124	27	151	31	164	19	400	30	265	25	196	24	139	23	142	34	186	26	272	26	168	16	151	20	115	29	140	26	117	27	201	20	195	22	108	34	133	31	224	25	155	30	173	31	146	28	178	22	198	19	361	22	342	19	498	13	286	20	296	23	356	20	368	24	73	53	278	22	135	24	171	33	210	41	167	31	50	42	158	25	70	54	187	54	222	19	331	26	265	31	152	24	363	31	150	37	237	59	153	36	177	14	235	16	245	13	118	35	125	29	118	31	127	32	133	19	69	42	157	32	132	26	0	.	190	2
81-120 years	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	.
	conifers	10	45	3	47	13	67	42	26	172	21	115	22	75	11	51	14	24	19	61	30	6	95	8	67	38	38	60	.	56	53	93	34	49	27	53	27	4	90	33	34	67	19	71	20	64	19	69	15	121	14	48	20	32	28	60	23	65	17	25	53	51	34	48	35	28	27	68	21	32	34	115	35	59	27	65	35	23	43	69	32	30	32	54	37	32	29	24	55	47	48	28	46	32	33	55	30	75	32	63	31	18	41	21	41	20	31	42	39	65	33	104	26	4	58	1	98	1	99	22	41	11	47	6	61	30	37	130	50	109	35	26	41	31	32	131	39	17	52	11	93	33	28	20	94	33	49	65	32	17	58	42	29	48	35	4	95	14	61	45	55	21	81	60	18	62	17	64	17	25	54	68	29	21	53	42	28	33	30	35	41	41	46	14	56	0	.	52	3
	broadleaves	41	24	17	29	31	47	31	33	21	45	49	33	24	26	17	18	25	25	40	26	27	47	78	50	57	35	0	.	23	88	12	32	28	30	42	34	58	61	25	34	9	54	14	38	1	70	1	43	1	100	48	22	21	31	9	39	22	34	41	48	12	47	26	30	9	52	13	40	46	34	2	68	57	50	33	36	94	44	31	38	25	38	65	38	76	28	38	47	108	35	53	36	77	39	14	46	12	77	45	32	28	33	25	41	33	31	1	97	5	43	1	97	22	49	31	32	23	33	30	55	24	37	16	34	25	36	0	.	6	67	33	59	8	59	52	40	28	59	3	93	54	30	31	63	10	96	14	43	26	37	60	35	30	68	56	48	3	96	55	15	14	74	0	100	1	70	11	36	20	53	44	30	15	41	27	30	30	44	45	37	41	51	20	42	0	.	25	5
	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	51	21	20	26	44	41	74	18	193	21	163	20	99	10	68	12	50	15	100	21	32	39	86	49	95	31	60	.	80	42	106	30	76	21	95	19	62	59	58	25	77	17	85	17	65	19	70	15	122	13	95	14	53	20	69	21	87	15	66	35	63	26	74	23	37	28	80	18	78	19	117	34	116	27	98	27	117	35	100	24	55	24	120	25	108	25	62	39	155	26	80	28	109	31	69	24	87	28	107	21	46	29	46	31	54	26	43	38	69	30	105	25	27	42	32	31	23	33	52	35	36	35	21	27	54	27	130	50	114	33	58	37	40	30	182	30	44	43	15	71	86	26	51	74	43	51	79	26	43	41	102	27	79	31	60	44	17	50	99	26	35	51	60	18	63	17	75	14	46	48	111	22	36	38	69	25	63	28	80	29	82	33	33	37	0	.	76	3
121-160 years	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	.
	conifers	1	99	0	.	1	97	16	43	41	54	31	56	41	15	19	19	16	22	29	29	0	.	7	97	8	41	0	.	2	96	5	52	12	41	7	43	0	.	50	47	34	18	33	34	54	24	52	20	60	14	19	29	8	63	15	45	45	33	21	45	34	34	31	45	24	31	33	31	31	50	31	47	53	24	9	55	20	63	21	41	44	39	28	34	10	56	14	48	20	71	9	97	7	50	27	36	47	30	23	41	1	98	0	.	15	40	20	45	31	35	48	26	8	89	1	98	1	99	11	41	14	59	18	71	13	42	106	74	37	40	25	91	42	40	47	33	14	54	0	.	33	40	0	.	17	58	32	26	27	42	21	36	14	41	32	83	10	66	21	85	13	94	41	20	59	19	45	23	5	69	0	.	15	54	15	51	6	99	6	97	0	.	8	83	0	.	27	4
	broadleaves	3	54	3	59	3	68	6	43	4	70	2	68	3	59	5	40	4	39	8	40	10	68	1	97	28	56	0	.	0	.	1	99	1	99	0	.	4	90	8	59	6	66	6	47	1	70	2	65	0	.	7	35	8	57	3	56	1	56	6	74	2	98	17	86	7	96	4	69	0	.	0	.	21	48	0	.	3	98	1	69	2	68	2	77	11	40	11	46	3	97	6	80	22	58	8	57	2	99	9	87	1	98	8	86	7	51	0	.	2	74	0	.	5	42	6	51	3	62	2	58	2	75	3	79	3	98	0	.	3	66	1	98	5	83	3	71	10	67	0	.	14	77	10	91	2	96	5	57	4	56	7	62	4	67	4	95	5	68	28	55	5	94	0	.	0	.	0	100	0	.	11	64	2	97	9	57	2	72	1	97	3	66	5	69	0	.	4	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	.
	total	5	63	3	59	4	54	21	35	45	51	33	54	44	14	24	17	20	19	37	27	10	68	8	97	36	46	0	.	2	96	6	52	13	40	7	43	4	90	58	42	40	18	39	29	55	24	53	19	60	14	25	24	16	42	18	40	46	32	27	37	37	35	48	53	30	38	36	31	31	50	31	47	74	24	9	55	23	58	22	39	45	38	31	32	21	44	25	39	23	67	15	72	29	48	35	39	49	30	32	38	2	68	8	86	22	39	20	45	32	33	48	26	13	56	7	49	4	57	14	34	16	60	22	64	16	36	106	74	39	40	26	88	47	37	50	33	24	45	0	.	47	35	10	91	19	54	37	23	32	38	28	33	19	39	36	84	15	44	49	66	19	94	41	20	59	19	45	22	5	69	11	64	17	59	24	41	8	83	8	83	3	66	13	71	0	.	32	4
>160 years	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	.
	conifers	0	.	1	99	0	.	3	98	11	96	4	66	28	19	6	30	2	64	9	45	0	.	0	.	0	.	0	.	0	.	0	.	7	53	6	79	0	.	17	39	33	28	15	36	33	27	25	18	45	18	1	52	3	70	0	.	3	63	0	.	3	71	11	65	3	97	9	71	1	98	26	51	8	56	0	.	5	82	10	62	22	55	20	50	1	99	1	98	0	.	0	.	0	.	29	40	27	60	0	.	0	.	0	.	0	.	23	40	22	30	26	33	1	98	0	.	0	.	16	39	6	58	8	64	15	51	93	60	50	65	14	58	17	58	6	55	0	.	0	.	4	98	0	.	0	.	26	29	0	.	9	57	29	42	0	.	0	.	0	.	0	.	50	18	26	23	33	26	0	.	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	14	6
	broadleaves	0	.	1	99	1	97	1	69	0	.	0	.	0	76	0	70	0	70	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	53	0	99	1	99	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	98	1	99	2	98	0	.	1	99	11	98	0	.	0	.	1	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	78	0	.	1	68	0	.	1	99	7	48	0	.	4	59	1	98	0	.	1	97	3	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	98	4	98	0	.	0	.	0	.	0	.	0	.	0	.	1	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	21
	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	.	2	72	1	97	4	85	11	96	4	66	28	18	7	29	2	62	9	44	0	.	0	.	0	.	0	.	0	.	0	.	7	53	6	79	0	.	23	35	34	27	16	36	33	27	25	18	45	18	2	51	3	70	0	.	3	63	0	.	3	71	11	65	3	97	9	71	1	98	26	51	9	52	0	.	6	68	12	62	24	50	20	50	2	99	12	98	0	.	0	.	1	97	29	40	27	60	0	.	0	.	0	.	0	.	23	40	28	27	26	33	2	54	0	.	1	99	23	30	6	58	12	46	16	47	93	60	51	64	17	52	17	58	6	55	0	.	0	.	4	98	0	.	0	.	26	28	0	.	10	51	33	39	0	.	0	.	0	.	0	.	50	18	26	23	33	25	0	.	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	15	6
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	.
	conifers	93	30	134	34	180	32	109	17	372	16	328	17	291	7	238	8	214	11	261	10	22	36	107	39	126	22	210	.	228	27	312	22	216	18	243	24	64	68	233	18	314	10	302	12	289	13	346	8	384	7	193	10	261	19	304	11	335	10	213	31	298	17	215	13	191	24	284	15	344	36	449	20	287	21	198	19	253	33	239	17	177	21	187	14	191	20	183	24	111	35	75	30	118	27	231	13	326	14	179	18	239	26	255	26	222	27	197	24	276	18	314	15	24	56	2	68	4	57	160	22	78	29	81	39	318	21	430	23	473	15	198	21	194	18	395	31	241	33	183	32	234	21	232	32	158	35	296	12	206	35	252	31	185	21	222	57	186	26	250	51	223	51	344	9	400	11	294	10	219	27	222	38	153	25	209	24	182	23	113	41	168	43	149	25	0	.	246	2
	broadleaves	202	12	160	17	230	24	178	18	70	35	170	24	111	13	131	11	156	9	264	10	231	22	296	17	330	15	0	.	194	24	172	25	216	20	156	19	314	25	199	17	64	26	92	24	127	31	117	21	33	43	206	11	177	15	116	24	173	16	234	25	147	21	163	26	171	23	89	22	292	24	40	36	194	26	198	18	296	21	211	20	359	25	225	17	262	16	250	17	265	19	226	20	325	15	196	22	62	29	191	30	253	16	175	18	173	16	225	42	173	27	113	37	518	15	484	13	648	9	359	17	411	17	483	15	298	25	119	67	77	43	241	25	199	27	224	30	263	29	207	37	187	18	127	33	200	44	87	24	234	26	223	17	165	24	372	23	155	39	148	33	179	43	58	26	83	23	172	15	111	37	198	19	172	18	173	17	145	17	218	22	205	20	216	24	0	.	183	2
	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	295	13	294	18	410	15	287	11	442	14	499	12	402	6	369	6	370	6	525	7	253	20	403	15	456	10	210	.	422	16	484	14	432	12	399	14	378	24	432	11	378	7	394	9	416	11	463	7	417	6	399	6	438	11	420	9	508	7	447	13	445	11	378	11	362	15	373	11	636	16	489	18	481	14	396	14	549	18	450	13	536	15	412	9	452	9	434	12	376	14	301	16	442	13	426	11	388	11	370	15	492	12	430	14	395	14	423	22	449	12	428	10	542	13	486	13	653	8	519	11	489	13	564	12	616	15	549	17	551	11	440	12	393	16	620	22	504	19	391	27	421	13	359	30	358	29	384	10	440	17	475	15	349	13	594	24	342	23	398	32	401	26	401	7	483	8	466	7	330	17	420	18	325	14	382	16	327	12	331	17	373	16	366	16	0	.	430	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. 2302947. Birmensdorf, Swiss Federal Research Institute WSL

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