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
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
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	.	4	100	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	.	1	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	54
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	0	.	1	99	2	99	2	99	0	.	0	.	0	.	0	.	0	.	0	.	4	96	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	.	2	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	1	57	0	100	0	100	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	0	.	1	70	2	99	2	99	0	.	0	.	0	.	0	.	0	.	0	.	4	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	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	27	46	106	27	77	35	59	38	67	49	87	41	32	14	60	15	100	15	40	21	20	75	53	64	21	36	0	.	45	51	82	34	59	28	88	37	5	95	37	24	64	20	41	26	123	22	51	15	46	20	77	17	103	30	148	20	66	24	44	60	77	32	23	53	31	35	100	51	17	36	66	40	61	36	69	37	86	48	46	28	26	27	30	32	87	34	57	39	18	39	13	50	45	42	49	38	73	26	34	42	76	31	113	36	99	52	52	29	40	29	59	40	8	60	2	70	1	99	19	29	12	59	18	61	57	45	22	43	51	34	94	35	46	42	68	46	105	38	140	32	105	30	66	45	57	38	19	33	15	55	12	37	27	45	123	56	80	45	0	.	100	48	50	20	32	20	41	19	116	32	118	55	76	33	80	39	52	47	42	57	77	53	22	68	0	.	56	4
	broadleaves	118	29	96	18	110	25	71	27	68	68	45	26	36	16	42	15	73	12	61	19	65	39	75	26	66	24	0	.	88	34	41	33	52	22	88	31	69	45	74	22	30	26	40	23	80	21	36	23	16	38	58	17	50	21	41	29	35	26	87	40	54	30	60	54	65	32	14	45	66	43	24	43	42	29	74	26	88	32	65	25	91	32	45	21	59	37	93	25	51	35	102	35	48	28	78	33	47	25	98	41	117	21	97	27	104	25	115	40	72	28	64	31	109	23	155	19	115	22	127	17	104	20	108	23	51	42	142	52	40	39	89	27	60	28	106	27	79	33	130	36	62	24	79	27	46	36	7	62	26	53	44	34	95	28	26	51	56	43	7	93	64	35	28	20	55	20	51	17	50	32	61	32	84	24	79	24	77	22	103	30	59	38	103	34	0	.	62	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	145	25	202	19	187	20	129	25	135	43	132	28	68	11	102	11	173	10	100	15	85	33	128	33	87	21	0	.	133	31	123	25	111	18	176	25	74	43	111	17	94	16	81	17	203	15	87	13	62	18	134	13	154	22	189	17	101	20	131	32	132	23	83	44	96	26	114	45	83	35	90	39	103	25	143	24	174	34	110	19	117	27	75	21	146	26	150	22	70	28	114	32	93	25	127	27	120	19	132	31	193	18	209	24	203	28	167	30	112	20	123	24	117	21	156	19	116	22	146	16	116	19	126	21	108	37	163	47	91	26	183	23	106	26	174	24	184	26	270	27	168	25	145	29	103	33	26	31	41	41	56	29	122	25	149	49	136	33	7	93	164	34	78	15	87	15	92	14	167	25	179	36	160	22	158	25	129	24	145	25	136	34	125	33	0	.	117	2
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	37	36	46	48	76	28	21	33	136	28	77	32	121	8	101	8	62	14	111	15	23	51	33	39	57	21	150	.	70	34	149	25	136	20	79	30	25	95	79	20	122	14	118	16	116	17	125	10	138	8	71	12	99	20	84	21	147	12	84	42	128	19	88	22	104	27	104	18	177	33	178	24	141	26	107	24	104	44	103	20	72	26	80	23	29	32	72	29	85	27	44	43	40	30	99	19	90	21	101	22	99	32	78	42	87	41	79	21	123	19	96	23	26	42	3	66	1	75	72	18	44	40	44	43	137	27	112	31	161	21	59	22	56	31	118	25	37	29	14	53	79	29	35	34	74	30	119	20	150	36	102	41	75	24	64	55	54	42	134	56	30	37	128	10	155	13	124	11	96	26	30	48	39	31	42	37	89	22	37	40	38	52	106	32	0	.	96	2
	broadleaves	83	15	86	23	58	23	109	23	54	34	64	27	61	14	63	12	61	11	144	12	82	31	106	18	152	19	0	.	77	26	82	30	87	24	80	28	196	24	64	18	26	27	26	28	29	34	50	18	14	45	112	11	104	21	65	28	93	18	105	32	58	27	62	30	57	33	49	18	97	27	31	37	84	24	76	22	70	26	74	20	143	27	91	21	132	16	61	29	106	25	82	28	130	17	127	20	40	41	59	31	94	27	63	30	52	22	66	31	73	23	61	30	249	17	262	15	414	10	156	16	229	17	275	16	130	29	15	60	32	43	78	29	135	31	77	30	108	24	58	63	114	28	80	34	125	42	52	25	132	28	103	20	88	21	201	33	63	39	58	45	62	37	24	24	37	26	103	14	34	26	75	31	66	28	77	23	46	20	56	39	77	32	39	25	0	.	84	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	119	16	132	22	135	18	130	21	190	22	141	22	183	7	165	7	123	9	255	10	105	27	140	16	210	15	150	.	147	20	230	18	223	14	160	19	222	26	143	13	149	12	144	14	145	14	175	9	152	8	183	9	203	16	149	19	240	10	190	23	186	15	151	21	161	21	153	13	274	22	208	20	224	18	183	17	174	27	177	16	215	19	171	20	161	15	133	21	191	20	126	30	170	15	227	15	130	20	160	22	193	20	140	26	139	26	145	18	196	14	157	16	275	15	265	15	415	10	228	12	273	15	320	14	267	23	127	31	193	19	137	18	191	26	195	20	145	20	72	56	193	22	115	26	199	30	171	18	282	22	205	22	163	16	265	30	117	31	192	37	92	29	152	9	192	11	226	9	130	20	104	25	104	22	120	23	135	16	93	31	115	26	145	23	0	.	180	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	17	31	8	52	12	40	29	23	131	18	103	18	71	9	54	10	22	16	55	21	17	47	10	34	22	33	60	.	31	44	91	26	62	26	47	24	4	64	43	25	63	16	76	14	53	14	63	10	93	10	46	14	43	31	49	21	67	14	30	36	43	24	85	34	27	25	70	15	30	41	83	30	51	22	39	32	58	31	69	19	24	24	64	19	34	23	20	37	39	34	22	35	36	27	72	30	66	23	57	23	27	44	14	42	19	30	28	30	41	27	57	23	3	43	3	88	0	100	19	23	10	53	16	49	42	29	71	42	79	29	43	25	22	28	82	23	11	42	20	61	32	24	26	67	28	36	77	19	28	39	32	25	32	31	5	67	29	49	44	34	14	54	55	11	62	13	60	16	50	30	62	25	28	34	44	21	31	26	27	35	41	33	26	36	0	.	49	2
	broadleaves	47	16	23	22	21	36	23	27	21	33	46	26	19	19	15	17	22	18	40	17	33	33	78	36	69	23	0	.	32	56	19	33	25	24	36	27	39	53	34	28	6	49	9	32	1	57	3	39	0	100	46	15	24	23	8	30	18	27	43	30	11	36	43	27	16	55	16	35	29	30	6	60	33	44	24	28	68	35	24	28	39	26	49	27	62	22	40	27	84	26	40	30	76	21	17	30	8	58	37	27	22	30	21	36	42	25	1	71	9	52	1	60	14	43	35	27	21	24	22	31	25	29	19	32	17	36	1	98	3	70	39	41	10	46	30	28	20	42	5	67	43	30	36	35	28	69	17	48	23	31	40	29	25	46	47	41	11	40	30	33	16	42	0	75	1	70	6	32	19	37	39	31	22	32	27	27	33	28	42	36	36	35	27	26	0	.	22	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	64	15	31	23	33	28	52	16	152	17	148	15	90	8	70	9	44	12	95	14	51	29	88	34	91	21	60	.	63	33	110	21	87	20	83	18	43	49	78	22	69	15	85	12	54	14	67	10	94	10	92	10	68	21	57	19	85	12	73	22	54	18	128	24	43	28	86	13	59	24	89	28	84	21	63	24	126	22	93	15	63	20	113	16	97	19	60	24	123	21	62	23	113	19	89	24	74	21	93	17	50	28	35	30	60	23	30	29	50	24	59	22	16	36	38	25	21	24	41	19	36	26	34	27	59	23	72	41	81	28	82	23	32	24	112	19	31	32	25	49	76	22	62	35	56	39	94	19	51	28	72	22	57	24	52	37	39	37	74	25	30	31	55	11	62	13	66	14	70	24	101	19	51	23	71	20	64	20	69	26	77	24	53	22	0	.	70	2
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	5	58	0	.	2	81	13	33	34	39	26	41	43	11	28	15	12	20	26	22	1	98	19	59	5	36	0	.	12	92	4	44	15	29	14	36	0	.	38	33	43	15	32	18	40	17	42	13	56	11	15	21	12	43	13	36	44	23	19	35	35	24	24	42	29	25	40	26	18	44	44	33	46	19	9	41	22	39	28	24	34	32	36	21	9	45	13	33	17	48	5	99	7	43	26	25	33	24	22	31	6	90	0	.	10	40	18	38	24	25	37	21	5	62	0	99	7	92	10	27	10	43	10	61	33	51	50	64	31	28	28	51	44	27	50	24	11	47	0	.	35	31	11	71	24	32	32	19	35	26	24	24	12	36	21	85	5	69	41	48	10	53	41	13	58	15	34	15	8	39	0	.	12	50	13	41	8	60	5	85	2	98	5	75	0	.	27	3
	broadleaves	8	31	6	61	2	57	6	37	5	54	4	42	3	39	4	30	4	28	9	28	5	71	14	46	25	34	0	.	1	98	1	70	2	71	6	56	4	64	7	39	3	61	5	51	0	70	1	51	0	.	6	26	8	42	4	55	2	56	4	63	3	52	11	88	5	83	3	57	0	.	0	.	14	44	5	59	4	53	2	40	5	56	6	34	10	33	13	29	7	52	9	45	15	41	9	37	1	99	7	64	1	99	5	87	6	46	0	.	4	42	0	.	7	41	4	39	4	46	5	30	5	44	10	47	2	99	0	.	1	68	1	69	5	63	5	78	15	43	0	.	9	79	11	73	2	68	3	59	5	40	11	42	4	49	4	72	4	57	16	54	10	69	0	.	0	.	1	57	1	98	8	54	3	58	8	43	2	51	1	68	2	68	6	48	0	.	4	6
	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	13	36	6	61	4	52	19	26	39	36	30	36	45	11	32	14	16	17	35	19	6	61	33	39	30	31	0	.	13	85	5	42	17	27	20	33	4	64	44	29	47	15	37	17	40	17	44	13	56	11	22	18	20	32	17	33	46	22	23	30	38	24	35	49	34	26	44	25	18	44	44	33	60	18	14	41	26	35	30	23	39	29	42	20	18	35	26	24	24	42	15	48	21	32	36	25	34	24	30	29	6	83	5	87	16	37	18	38	28	22	37	21	12	34	5	38	11	58	15	21	15	34	19	40	35	48	50	64	32	28	29	49	49	25	55	23	26	35	0	.	44	28	22	48	26	31	34	18	39	25	35	21	15	31	25	82	10	43	57	38	20	51	41	13	58	15	35	15	9	36	8	54	15	48	22	32	10	51	6	67	3	58	11	55	0	.	31	3
>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	.	0	99	0	.	1	99	10	70	3	58	22	14	7	23	1	65	5	42	0	.	0	.	0	.	0	.	2	98	0	.	5	45	3	80	0	.	18	35	31	22	20	27	22	20	28	11	41	13	1	40	2	61	0	.	3	39	0	.	4	53	7	67	2	98	10	41	4	77	26	33	15	32	0	.	5	50	14	45	11	57	15	38	4	84	0	99	0	.	0	.	0	.	16	37	13	60	4	66	0	.	0	.	0	.	22	24	17	21	23	23	1	59	0	.	0	.	14	25	5	38	6	44	10	50	52	47	33	54	14	56	21	30	21	38	1	98	0	.	4	63	0	.	1	98	25	26	9	49	10	37	21	34	0	.	0	.	5	93	0	.	42	12	32	17	25	19	1	98	0	.	0	.	2	69	0	99	1	98	0	.	0	.	0	.	14	4
	broadleaves	1	78	2	59	0	99	1	70	0	.	0	.	1	45	0	58	0	70	0	100	0	.	1	99	5	99	0	.	0	.	0	.	2	62	0	.	0	.	3	47	0	75	0	100	0	.	0	54	0	.	0	74	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	99	0	.	0	99	0	.	1	99	1	99	1	99	0	.	0	99	6	99	0	.	0	.	1	70	1	72	0	.	0	.	0	.	0	.	0	.	0	.	3	57	0	.	1	64	3	99	2	61	5	39	1	79	3	45	1	99	0	.	1	99	2	99	0	.	1	99	0	.	0	.	0	.	4	98	0	.	1	60	1	99	1	99	2	99	0	.	0	.	5	93	0	.	0	100	0	.	0	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	98	0	.	1	14
	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	1	78	2	51	0	99	2	85	10	70	3	58	23	13	7	22	1	63	5	41	0	.	1	99	5	99	0	.	2	98	0	.	6	39	3	80	0	.	21	31	32	21	21	27	22	20	29	11	41	13	2	39	2	61	0	.	3	39	0	.	4	56	7	67	2	98	10	41	5	79	26	33	15	31	0	.	5	45	15	45	12	52	15	38	5	79	6	99	0	.	0	.	1	70	17	35	13	60	4	66	0	.	0	.	0	.	22	24	20	19	23	23	3	43	3	99	2	61	18	21	6	40	9	33	11	47	52	47	34	53	16	51	21	30	22	38	1	98	0	.	4	63	4	98	1	98	26	26	9	46	10	35	23	33	0	.	0	.	10	60	0	.	43	12	32	17	26	19	1	98	0	.	0	.	2	69	0	99	1	98	0	.	1	98	0	.	15	4
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	87	23	160	21	167	19	123	19	379	11	295	13	289	5	251	5	198	8	237	9	62	35	115	32	105	16	210	.	160	22	326	16	276	12	231	17	34	70	215	12	328	7	288	8	355	9	311	6	375	5	210	8	260	13	294	10	328	8	177	24	286	12	227	16	192	18	325	16	246	25	396	14	313	15	224	15	275	23	261	12	167	15	225	12	163	18	162	18	159	20	84	25	128	19	263	11	275	10	218	13	208	18	204	23	215	27	200	14	245	13	271	13	43	37	9	67	8	74	134	13	82	25	94	27	279	16	307	18	354	14	238	15	188	15	339	17	164	25	174	28	257	15	137	25	184	20	272	9	236	23	179	23	167	15	213	40	168	23	224	34	154	30	316	6	339	8	285	7	271	16	209	30	155	17	181	19	180	16	112	28	158	28	158	22	0	.	241	1
	broadleaves	256	14	213	12	192	15	209	14	148	33	159	17	120	11	125	9	160	7	253	8	188	18	276	13	318	11	0	.	198	18	142	21	168	16	210	17	309	20	182	13	66	18	81	18	110	19	91	14	30	30	223	8	187	14	118	19	148	14	240	17	127	20	176	19	143	19	82	17	193	21	62	28	173	19	179	14	231	17	165	14	278	18	191	14	263	11	212	14	248	14	233	17	270	11	233	14	96	26	200	21	237	15	185	15	203	13	182	28	161	16	126	22	379	11	463	9	556	7	315	10	365	12	416	11	203	26	157	47	77	30	208	19	210	22	219	18	223	19	198	28	228	18	210	15	201	29	80	21	185	21	199	14	214	15	277	23	134	29	116	30	152	25	53	17	92	16	161	10	105	21	182	16	175	14	191	14	158	11	202	18	173	17	176	19	0	.	173	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	342	11	374	11	359	9	331	11	526	10	454	9	409	4	376	4	358	5	490	6	249	15	390	11	423	8	210	.	358	12	468	11	444	8	441	10	343	20	397	8	393	6	369	7	465	7	402	5	404	5	432	5	447	9	412	8	476	6	416	9	414	9	403	10	336	11	407	12	439	14	458	12	487	10	404	9	506	13	425	9	445	11	416	11	427	8	375	10	407	11	317	15	398	9	496	8	371	9	418	10	444	9	389	12	418	13	382	15	406	9	398	8	422	10	471	8	564	7	449	7	447	11	510	9	481	15	464	16	431	11	447	9	399	13	558	11	387	13	372	21	485	12	347	15	385	19	351	9	422	13	378	12	381	9	490	20	302	17	340	22	307	18	369	5	431	6	446	5	376	12	392	14	330	9	372	12	339	8	314	13	331	13	334	13	0	.	413	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. 2302661. Birmensdorf, Swiss Federal Research Institute WSL

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