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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	.	6	99	0	.	1	70	0	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	55
	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	.	7	98	0	.	0	.	2	99	3	99	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	.	6	99	0	.	2	57	0	100	0	100	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	.	7	98	0	.	0	.	3	69	3	99	3	98	0	.	0	.	0	.	0	.	0	.	0	.	7	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	34
=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	35	47	10	52	95	53	28	19	61	21	121	18	39	24	5	96	43	74	19	49	0	.	59	74	64	35	72	35	70	66	7	93	43	32	57	20	43	34	86	27	66	15	44	26	57	20	103	44	139	23	72	35	67	63	72	49	35	49	39	41	59	26	14	48	40	42	53	63	62	51	132	51	36	44	17	40	31	35	108	35	92	45	5	67	4	73	44	59	43	51	87	41	15	47	90	44	129	44	62	58	23	42	40	47	21	75	7	99	0	.	1	99	32	31	18	69	30	65	88	42	29	48	37	37	108	39	32	35	112	57	161	50	153	39	86	43	141	47	56	62	16	36	19	72	11	50	25	51	145	69	87	55	0	.	142	86	53	32	42	21	33	26	135	42	118	71	65	58	98	45	58	68	50	72	78	83	38	69	0	.	56	5
	broadleaves	87	29	78	26	138	42	48	31	12	56	46	34	32	19	50	18	63	15	48	19	95	43	100	33	71	28	0	.	65	57	54	38	59	29	51	33	43	65	97	26	27	36	52	30	92	31	46	31	12	50	56	25	56	26	29	26	39	34	95	56	67	39	85	56	101	29	25	43	106	45	11	63	30	50	87	28	116	34	77	32	122	38	51	24	62	52	128	28	53	63	84	46	48	42	44	48	51	33	116	55	126	30	80	37	78	30	140	56	97	37	38	50	124	30	149	27	125	34	141	25	112	22	148	27	76	42	237	52	25	49	110	34	79	30	131	39	107	39	165	43	60	31	67	39	53	54	3	69	13	48	53	48	43	51	25	69	72	54	13	85	53	44	28	29	56	24	59	24	52	47	56	31	88	31	70	27	65	32	125	38	44	53	150	37	0	.	64	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	83	31	22	40	141	38	60	14	111	15	184	14	88	16	99	41	143	40	90	23	0	.	125	56	118	26	131	23	120	42	49	61	140	22	84	18	95	21	178	20	113	15	56	23	112	16	159	31	168	20	111	28	162	40	139	29	120	44	141	24	84	23	120	40	50	42	84	45	149	35	248	39	113	27	139	36	82	22	171	29	220	26	58	58	88	44	93	35	87	42	138	26	130	48	215	26	209	32	140	33	163	49	137	29	60	40	131	28	149	27	126	34	173	21	130	20	177	24	164	35	266	49	62	31	218	24	111	27	243	32	268	35	319	32	146	33	207	43	109	49	19	37	32	46	64	43	68	36	169	63	159	40	13	85	195	59	81	24	98	18	92	21	187	31	173	46	153	32	168	27	124	34	175	31	122	54	188	35	0	.	120	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	144	41	83	42	125	11	100	11	49	20	120	17	11	54	49	52	52	32	150	.	111	40	161	30	110	35	107	37	35	93	92	25	113	19	116	20	95	27	125	14	131	10	68	19	104	28	90	24	153	15	99	55	128	28	89	28	97	37	116	26	238	46	232	28	125	31	61	33	65	61	99	27	65	32	53	24	39	37	52	45	38	37	34	52	27	35	99	22	82	37	91	27	131	41	93	48	118	46	74	36	111	25	106	23	25	61	0	.	2	74	93	23	29	46	36	52	172	33	94	39	179	26	55	30	61	32	103	53	49	40	18	62	83	41	47	53	51	56	148	22	134	53	152	49	69	36	71	67	76	45	184	77	47	51	129	16	173	18	116	18	103	42	28	77	52	39	49	49	84	28	22	58	40	77	90	35	0	.	96	3
	broadleaves	70	22	62	30	57	35	96	29	31	53	74	37	59	19	58	16	65	15	161	15	95	45	116	24	197	24	0	.	105	38	100	35	112	28	63	37	163	36	61	21	17	42	33	39	21	57	49	26	22	50	96	18	92	25	75	33	105	22	93	49	60	25	35	30	54	50	48	26	119	31	23	45	85	35	78	30	71	39	92	27	208	34	91	20	111	25	62	42	101	30	83	29	160	22	133	26	51	59	50	45	94	27	54	26	51	23	61	43	64	40	61	45	254	24	292	20	473	13	140	26	214	26	260	22	176	31	16	85	30	62	76	35	89	45	60	33	118	37	31	46	69	24	27	49	136	66	62	31	180	33	94	24	83	29	258	35	75	50	53	85	106	48	24	36	27	36	100	19	32	51	90	33	66	41	76	29	48	26	47	55	106	38	41	31	0	.	85	3
	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	108	27	175	36	158	28	184	10	158	9	114	11	281	12	106	40	165	22	248	19	150	.	217	24	260	21	222	21	170	25	198	39	153	18	130	17	150	16	117	24	174	12	153	11	164	13	196	20	165	22	258	13	192	33	188	19	124	27	151	31	164	19	357	29	255	25	210	21	139	23	135	33	191	23	272	26	144	17	151	20	115	29	140	26	117	27	187	21	232	20	133	34	141	27	224	25	147	31	169	31	135	27	175	20	167	19	279	22	292	20	474	13	234	18	243	23	297	20	348	25	110	41	209	25	132	23	150	33	163	41	167	31	50	42	152	26	74	46	187	54	210	19	313	26	247	30	152	24	329	32	150	37	237	59	153	36	154	14	200	16	216	13	135	32	117	30	118	31	125	31	133	19	69	42	146	33	132	26	0	.	181	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	25	175	20	115	22	72	10	50	14	24	19	59	30	6	95	8	67	31	39	60	.	56	53	89	34	44	28	53	27	3	93	32	32	57	19	69	20	54	19	60	14	108	13	48	20	37	28	60	23	64	17	25	53	49	34	48	35	28	27	68	21	27	36	113	34	58	25	65	35	37	46	70	27	30	32	56	31	32	29	24	55	47	48	28	46	29	33	52	27	70	31	59	30	18	41	20	41	20	32	41	36	54	33	84	26	3	58	1	99	1	99	20	34	9	45	8	46	28	38	103	46	80	37	29	37	29	31	99	41	17	52	11	93	32	29	18	94	33	49	64	31	21	48	42	27	48	35	3	96	14	61	45	55	21	81	54	18	52	17	55	17	28	45	64	30	21	53	40	29	33	30	35	41	38	47	14	56	0	.	49	3
	broadleaves	41	24	17	29	31	47	32	31	19	45	49	33	24	24	17	18	25	25	39	26	27	47	78	50	56	32	0	.	23	88	12	32	25	31	42	34	41	65	22	34	8	54	14	35	1	70	1	39	1	100	48	22	19	32	9	39	21	34	41	48	12	47	26	30	9	52	13	40	38	35	2	68	52	50	33	36	86	44	28	36	25	38	62	34	76	28	38	47	108	35	53	36	72	38	13	42	11	78	41	33	28	33	24	41	36	29	1	98	4	43	1	69	17	47	27	33	22	33	20	55	20	37	13	34	23	37	2	96	4	68	30	59	8	54	39	41	28	59	3	93	52	30	27	65	10	96	13	43	24	37	54	36	30	68	51	49	3	96	55	15	14	74	0	100	1	70	9	35	19	48	41	31	15	41	27	29	30	44	45	37	38	52	20	42	0	.	22	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	17	194	19	163	20	96	10	67	12	49	15	98	21	32	39	86	49	87	29	60	.	80	42	100	30	69	22	95	19	44	63	53	24	64	17	83	17	54	18	61	14	109	13	95	14	56	20	69	21	86	15	66	35	60	27	74	23	37	28	80	18	66	21	115	33	110	26	98	27	123	32	98	21	55	24	118	22	108	25	62	39	155	26	80	28	101	31	65	22	81	28	100	21	46	29	44	32	56	25	42	35	58	31	86	26	20	41	27	32	22	33	40	31	29	34	21	25	51	27	105	45	84	35	58	34	36	28	139	32	44	43	15	71	83	26	45	75	43	51	76	25	46	37	96	26	79	31	54	45	17	50	99	26	35	51	54	18	52	17	64	15	47	41	104	23	36	38	67	25	63	28	80	29	76	34	33	37	0	.	72	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	15	43	40	52	31	56	39	14	19	19	16	22	29	28	0	.	7	97	7	42	0	.	2	96	5	53	11	41	7	43	0	.	45	46	31	18	30	31	43	24	43	19	50	14	19	29	7	63	15	45	48	30	21	45	33	34	31	45	24	31	33	31	30	45	29	47	50	24	9	55	27	50	17	41	44	39	33	29	10	56	14	48	20	71	9	97	7	44	25	34	42	31	22	39	1	98	0	.	15	40	16	47	27	34	37	28	7	77	1	98	1	99	8	39	11	59	15	71	12	42	81	67	28	41	24	84	39	37	36	35	14	54	0	.	31	40	0	.	17	58	33	24	27	40	21	33	14	41	29	83	10	66	21	85	13	94	35	20	49	20	38	22	4	70	0	.	15	54	15	51	6	99	6	97	0	.	8	83	0	.	26	4
	broadleaves	3	54	3	59	3	68	6	39	3	70	2	68	3	59	5	40	4	39	8	35	10	68	1	97	25	52	0	.	0	.	1	99	0	99	0	.	3	93	7	59	5	66	5	47	1	70	2	56	0	.	7	35	8	57	3	56	1	56	6	74	2	98	17	86	7	96	4	69	0	.	0	.	19	49	0	.	3	98	1	70	2	68	4	62	11	40	11	46	3	97	6	80	22	53	8	54	2	99	8	87	1	98	8	86	7	51	0	.	1	74	0	.	4	44	5	52	3	62	2	59	2	75	3	79	3	98	0	.	2	67	1	98	4	83	2	72	10	67	0	.	14	77	9	92	2	96	5	57	4	56	6	62	4	67	4	96	5	68	28	55	5	94	0	.	0	.	1	84	0	.	10	64	2	97	8	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	22	33	43	49	33	54	41	14	24	17	20	19	37	26	10	68	8	97	32	44	0	.	2	96	6	52	12	40	7	43	3	93	52	41	35	17	35	28	44	24	45	18	50	14	25	24	15	42	18	40	49	30	27	37	35	35	48	53	30	38	36	31	30	45	29	47	69	24	9	55	30	47	18	39	45	38	37	28	21	44	25	39	23	67	15	72	29	45	32	36	44	31	30	37	2	68	8	86	21	39	16	47	28	33	37	28	10	51	6	49	4	57	10	33	13	60	18	64	15	37	81	67	30	40	25	82	43	34	38	35	24	45	0	.	45	35	9	92	19	54	38	21	31	36	27	31	19	39	33	85	15	44	49	66	19	94	35	20	49	20	39	22	4	70	10	64	17	59	23	41	8	83	8	83	3	66	13	71	0	.	29	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	.	2	98	17	67	4	66	26	18	6	30	2	64	9	43	0	.	0	.	0	.	0	.	0	.	0	.	7	49	6	79	0	.	15	37	27	27	15	33	27	27	22	17	38	18	1	52	3	70	0	.	3	56	0	.	3	71	11	65	3	97	9	71	2	68	26	49	8	53	0	.	5	71	13	52	22	55	21	43	1	99	1	98	0	.	0	.	0	.	24	41	24	60	0	.	0	.	0	.	0	.	25	33	20	29	23	29	1	70	0	.	0	.	11	38	5	53	8	55	14	52	66	59	37	67	12	58	17	50	9	53	0	.	0	.	4	98	0	.	0	.	25	29	0	.	11	47	29	42	0	.	0	.	0	.	0	.	44	17	24	22	28	25	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	.	10	98	0	.	0	.	0	.	0	.	0	.	0	.	5	53	0	100	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	1	99	0	.	0	.	0	.	0	.	0	.	0	.	5	72	0	.	1	69	0	.	1	99	5	49	0	.	3	60	1	98	0	.	1	98	3	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	1	98	4	98	0	.	0	.	0	.	0	.	0	.	0	.	0	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	17	67	4	66	26	18	6	30	2	62	9	42	0	.	0	.	10	98	0	.	0	.	0	.	7	49	6	79	0	.	21	34	28	27	16	33	27	27	22	17	38	18	2	51	3	70	0	.	3	56	0	.	3	71	11	65	3	97	9	71	2	68	26	49	8	49	0	.	6	60	14	52	24	50	21	43	2	99	12	98	0	.	0	.	1	97	25	39	24	60	0	.	0	.	0	.	0	.	25	33	25	26	23	29	2	48	0	.	1	99	16	30	5	53	11	41	15	48	66	59	38	65	15	52	17	50	9	53	0	.	0	.	4	98	0	.	0	.	25	28	0	.	12	43	33	39	0	.	0	.	0	.	0	.	44	17	24	22	29	24	0	.	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	14	5
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	107	17	386	15	328	17	290	6	237	8	212	11	256	10	22	36	107	39	109	23	210	.	228	27	319	20	245	19	243	24	45	72	227	17	291	9	274	12	307	11	317	8	372	7	193	10	254	19	304	11	339	10	213	31	284	18	215	13	191	24	284	15	310	34	439	19	294	20	198	19	266	30	235	17	177	21	194	13	191	20	183	24	111	35	75	30	108	27	242	12	305	14	187	16	239	26	242	27	215	27	179	22	252	18	272	15	43	54	1	69	4	57	166	16	74	27	97	32	314	20	372	22	360	18	228	19	177	17	359	30	241	33	183	32	236	21	205	34	158	35	285	12	201	34	237	30	185	21	248	48	186	26	250	51	223	51	317	9	340	11	271	10	270	23	209	39	153	25	203	24	182	23	113	41	156	44	149	25	0	.	241	2
	broadleaves	202	12	160	17	230	24	183	17	65	35	170	24	118	13	130	11	157	9	257	10	231	22	296	17	358	14	0	.	194	24	166	25	196	21	156	19	249	28	192	16	56	25	104	23	115	27	98	20	34	38	206	11	176	15	116	24	168	16	234	25	141	21	163	26	171	23	89	22	264	24	38	37	187	25	198	18	277	21	199	18	359	25	208	16	262	16	250	17	265	19	226	20	303	16	199	21	114	38	215	29	253	16	165	18	172	15	201	39	171	25	100	34	400	16	481	12	623	9	307	16	349	17	429	14	281	26	255	48	62	41	220	26	180	26	233	26	263	29	207	37	194	17	130	28	200	44	83	24	221	27	209	17	165	24	337	25	155	39	148	33	179	43	53	25	84	21	170	14	104	35	196	18	172	18	182	16	145	17	218	22	191	21	216	24	0	.	176	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	290	10	452	13	499	12	408	6	367	6	370	6	513	7	253	20	403	15	467	10	210	.	422	16	484	13	441	12	399	14	294	28	419	10	347	7	379	9	422	9	415	6	406	6	399	6	430	11	420	9	507	7	447	13	425	12	378	11	362	15	373	11	574	16	477	17	482	13	396	14	543	17	434	13	536	15	402	9	452	9	434	12	376	14	301	16	411	14	441	12	419	12	401	16	492	12	407	15	387	14	380	21	424	12	373	11	443	14	482	12	627	9	473	10	424	13	526	11	596	15	627	16	422	15	448	11	356	15	592	20	504	19	391	27	431	13	335	29	358	29	368	10	422	17	446	15	349	13	585	22	342	23	398	32	401	26	369	8	423	8	440	7	374	15	405	17	325	14	385	15	327	12	331	17	347	18	366	16	0	.	418	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. 2302052. Birmensdorf, Swiss Federal Research Institute WSL

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