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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NFI3
growing stock (stemwood)
diameter at breast height (main diameter classes) · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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
diameter at breast height (main diameter classes)	conifers and broadleaves	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	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	92	67	40	62	45	75	29	52	9	327	43	56	191	19	202	16	198	22	133	26	2	100	16	72	15	37	0	.	5	58	84	45	63	36	36	47	0	.	43	30	86	27	57	51	74	33	289	22	230	24	100	25	118	36	93	24	110	33	7	129	51	39	13	56	18	57	45	31	35	58	38	44	59	53	24	55	29	60	33	42	12	81	11	266	68	47	42	45	11	87	4	59	22	46	36	54	54	45	8	56	58	42	37	37	96	52	22	42	35	41	32	53	2	100	0	.	0	.	32	33	9	51	6	86	22	114	6	58	48	39	42	64	24	52	23	71	18	48	10	76	46	40	23	73	8	61	44	39	34	80	29	48	27	42	24	66	13	58	0	.	0	.	226	22	221	25	98	19	18	39	38	56	12	53	13	44	64	79	6	82	4	71	29	54	0	.	4520	5
	broadleaves	44	38	24	36	14	190	69	44	7	64	21	49	269	45	100	25	124	18	180	27	22	49	30	38	58	28	0	.	11	46	54	60	37	32	30	43	32	77	78	38	25	35	17	35	89	43	191	37	31	54	208	24	30	37	51	48	76	29	19	83	44	34	16	59	21	43	21	46	28	45	12	59	62	49	25	38	40	46	74	36	41	43	42	32	97	36	50	36	34	45	27	35	20	52	57	44	20	41	24	59	52	34	10	43	108	79	72	65	-10	321	20	48	167	33	107	36	293	27	142	29	124	31	188	29	30	37	26	81	11	45	32	48	43	41	17	44	26	43	10	73	27	33	2	73	36	62	21	51	22	86	33	36	25	45	47	55	10	63	3	100	5	77	106	39	52	36	152	21	10	82	14	54	40	77	37	29	22	70	31	61	7	58	39	44	0	.	4904	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	.	0	0
	total	136	47	64	42	58	72	98	46	15	186	64	42	460	28	302	14	321	17	313	21	24	49	46	42	74	25	0	.	17	37	138	36	100	27	66	36	32	77	120	29	111	25	74	41	163	28	480	20	261	22	308	20	148	32	144	26	186	24	26	72	96	30	29	41	38	36	67	30	63	39	51	39	121	37	50	40	69	47	107	30	53	42	53	61	165	29	91	34	45	41	32	36	42	35	93	38	74	36	31	49	110	31	47	32	204	48	95	51	25	150	52	38	169	33	107	36	293	27	174	25	133	30	194	29	52	64	32	73	59	34	74	42	66	37	40	44	43	38	20	72	72	33	25	68	43	56	65	33	56	59	62	32	53	37	71	47	23	57	3	100	5	77	332	20	273	22	249	16	28	39	52	44	52	58	50	29	87	61	37	53	11	45	68	35	0	.	9424	4
16-23 cm	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	109	51	164	52	106	60	68	52	78	43	208	38	663	15	572	23	615	22	425	35	5	100	22	100	42	63	0	.	58	60	243	35	212	35	134	44	7	100	258	44	416	25	333	28	252	28	711	17	975	15	431	23	204	31	358	28	453	27	34	99	103	29	99	55	47	42	216	39	192	54	220	31	158	36	58	68	193	44	128	39	37	82	77	44	364	41	86	38	74	49	39	58	87	40	65	64	271	40	96	38	212	39	191	42	326	41	48	57	164	29	126	34	0	.	0	.	0	.	99	53	36	47	23	48	97	46	38	55	241	32	92	42	26	42	114	49	53	73	18	80	90	42	40	52	30	52	181	29	95	57	188	59	67	58	126	67	57	51	8	100	10	100	816	20	773	22	558	21	159	50	57	61	53	65	186	81	98	56	87	74	14	58	89	44	0	.	16451	4
	broadleaves	214	33	140	34	105	32	206	43	20	65	98	44	498	23	437	21	662	19	599	20	99	57	67	42	160	44	0	.	72	39	193	30	253	37	168	37	84	55	352	46	49	36	93	41	189	36	319	25	134	45	457	25	136	29	131	33	251	32	30	83	97	41	14	131	45	43	63	40	167	52	9	84	173	38	134	43	179	35	122	44	236	44	203	34	214	30	71	44	155	37	128	41	145	34	102	52	50	50	144	44	195	43	93	47	122	43	73	45	126	47	80	52	411	30	416	29	687	20	404	27	503	35	481	26	137	40	34	81	10	118	131	43	73	41	98	41	22	149	36	108	115	38	7	78	52	62	77	43	138	49	113	37	72	39	203	45	78	55	6	100	34	61	104	35	179	32	373	21	47	55	73	38	69	69	85	39	77	32	132	46	189	49	67	60	0	.	15092	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	.	0	0
	total	323	30	304	38	211	35	274	36	98	38	306	31	1161	14	1010	17	1277	16	1024	20	104	55	89	40	202	39	0	.	130	46	436	25	465	28	302	30	92	53	609	33	465	23	427	25	440	23	1030	14	1109	15	887	18	341	23	489	25	705	22	64	66	199	29	113	54	92	38	279	32	359	37	229	31	331	28	192	39	372	32	250	33	273	41	280	31	578	29	157	33	228	34	167	36	231	28	167	44	321	35	240	35	407	30	284	38	449	32	121	37	290	27	206	29	411	30	416	29	687	20	503	25	539	33	504	25	234	39	72	48	250	32	223	37	100	32	212	44	75	77	53	74	205	31	47	51	82	44	258	28	232	38	301	40	138	43	329	40	136	47	14	71	44	67	920	19	952	19	931	16	205	41	130	34	122	50	271	58	175	35	219	41	202	47	156	36	0	.	31543	3
24-35 cm	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	277	46	125	60	191	54	82	42	559	36	659	36	2544	13	1970	18	1787	21	1316	23	0	0	132	51	113	41	55	.	305	47	760	34	394	35	381	39	93	90	889	50	1090	22	900	24	768	23	2523	16	2668	14	1201	24	505	29	949	27	1345	26	181	55	501	42	151	46	178	51	357	33	387	39	430	34	657	34	331	36	242	52	613	31	241	37	532	26	506	35	202	46	248	45	53	92	173	40	126	58	601	32	240	53	559	46	423	39	352	66	28	73	492	33	389	39	64	59	0	.	0	0	329	35	156	55	148	52	325	38	313	61	558	36	174	44	256	40	390	51	181	47	64	64	305	35	163	56	117	58	586	28	92	42	195	38	196	52	64	100	253	56	62	54	78	79	1741	16	1896	18	1554	18	144	60	165	47	217	54	495	34	473	32	96	53	227	64	163	49	0	.	46014	3
	broadleaves	482	36	256	41	222	40	576	31	52	66	488	33	1059	27	1084	19	1518	17	1496	19	213	51	342	42	555	30	0	.	89	67	350	36	649	36	493	39	278	53	603	34	186	42	138	36	122	48	597	28	129	54	1224	20	260	35	467	39	600	33	87	49	168	35	110	52	124	73	162	37	515	43	30	77	416	36	457	33	591	37	347	35	392	40	784	29	876	29	291	45	394	37	273	39	727	31	259	42	98	52	204	37	426	40	269	37	361	31	19	73	179	36	109	59	362	30	742	27	946	23	436	31	584	33	604	30	314	40	15	100	69	70	282	40	159	53	234	43	266	49	39	60	474	32	14	100	63	66	108	68	350	33	369	35	239	54	240	40	84	55	54	85	41	74	192	43	57	58	465	28	94	54	212	40	209	37	455	32	346	35	158	51	200	53	114	41	0	.	32787	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	759	35	380	35	413	35	659	28	612	35	1147	28	3603	12	3054	15	3305	14	2812	17	213	51	474	38	668	29	55	.	394	41	1110	26	1043	26	874	27	371	48	1491	34	1276	20	1038	22	890	21	3120	14	2797	14	2425	15	765	24	1416	24	1944	22	268	43	669	34	260	37	302	48	519	28	902	33	460	33	1072	26	788	27	833	30	961	24	633	29	1316	24	1383	24	494	35	642	30	327	36	900	29	385	33	699	30	443	35	985	33	692	31	713	36	48	62	671	27	499	33	427	27	742	27	946	23	765	24	740	28	752	26	639	33	328	58	627	34	456	30	414	34	623	40	446	39	103	54	779	26	178	57	180	52	695	27	443	30	564	29	434	38	304	38	337	49	116	66	119	57	1933	15	1952	18	2020	16	238	43	377	36	427	40	950	27	818	26	254	40	427	43	277	39	0	.	78801	2
36-51 cm	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	320	36	399	64	304	52	1147	27	1125	30	1942	24	6191	12	4218	13	3920	15	2758	17	37	76	69	61	609	47	67	.	458	45	1193	31	1209	28	1278	29	14	100	1335	26	2843	21	1454	20	2158	21	4489	14	5115	12	2657	19	816	30	2268	21	2670	21	222	50	884	27	458	38	266	47	1098	27	351	35	428	34	1040	29	1192	32	406	37	940	31	606	44	1230	26	894	27	467	37	396	41	213	53	393	32	753	31	1222	28	625	32	493	35	480	39	446	32	292	47	736	28	934	30	94	67	0	.	54	76	465	33	281	39	220	47	312	41	451	55	821	32	584	49	689	39	617	35	343	48	122	75	560	30	283	70	382	50	798	24	188	46	475	32	555	32	273	85	320	49	120	61	134	66	2701	16	2615	16	2732	16	261	41	269	41	574	32	841	30	1187	26	415	36	431	56	296	39	0	.	91998	2
	broadleaves	1302	30	635	28	276	53	888	28	150	49	289	43	1192	23	1370	21	2591	15	1449	19	334	49	614	39	545	29	0	.	279	66	519	32	487	35	622	34	176	76	1085	37	319	45	256	37	119	55	293	28	56	60	2560	16	1082	51	325	37	637	35	274	49	108	54	161	45	99	53	189	42	246	47	49	67	531	36	665	33	865	40	440	29	599	35	1144	38	1013	24	540	35	517	33	656	34	857	28	442	35	131	94	462	36	423	32	419	37	501	29	7	100	154	33	54	83	79	57	497	26	893	31	351	42	268	33	478	30	188	63	11	100	65	64	415	46	112	50	137	53	176	50	39	114	573	30	147	59	100	50	113	47	345	41	583	33	339	48	478	41	206	53	191	55	132	48	74	49	39	87	274	40	282	46	459	36	322	31	516	29	537	28	359	35	456	40	362	35	0	.	42587	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	.	0	0
	total	1623	26	1034	31	579	39	2035	21	1276	29	2231	23	7383	11	5589	12	6511	11	4207	15	371	47	683	38	1154	31	67	.	737	40	1712	24	1696	24	1900	24	190	71	2420	23	3162	19	1710	19	2277	20	4782	13	5171	12	5216	13	1898	32	2593	20	3308	19	496	37	992	27	619	34	365	41	1287	25	596	31	478	31	1571	24	1857	26	1270	31	1380	24	1204	32	2374	24	1907	21	1006	28	913	28	869	32	1250	26	1195	25	1353	27	1087	26	916	28	899	31	947	26	298	46	890	24	988	29	174	47	497	26	946	30	816	26	549	31	697	27	500	33	462	53	885	31	999	36	801	34	754	32	519	41	161	62	1133	24	430	56	481	45	910	22	533	34	1057	26	895	28	750	43	526	41	311	54	266	48	2776	16	2654	16	3006	15	543	40	728	31	896	28	1357	26	1724	22	774	31	888	37	658	31	0	.	134585	2
=52 cm	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	336	44	261	40	347	56	1832	27	1066	33	1399	27	6206	14	6534	14	5749	15	1863	19	21	100	213	88	435	50	31	.	308	58	822	37	1321	31	1013	30	0	0	1879	28	2656	22	1804	23	3149	19	4973	15	3677	16	2369	20	1266	39	2267	24	2401	24	370	43	819	31	558	45	587	49	1400	27	442	43	415	45	1100	33	810	44	424	50	1177	32	1563	32	2737	29	659	33	874	44	235	50	176	56	106	79	1366	31	1194	32	948	43	226	44	582	45	941	33	749	39	1186	33	858	35	92	60	60	71	79	73	560	38	274	44	402	67	461	45	411	46	529	41	879	34	721	36	744	35	675	53	14	100	896	39	55	100	345	59	524	33	562	50	388	38	668	41	133	79	524	66	318	74	345	80	3783	18	1943	19	2774	19	290	55	774	40	545	37	697	35	694	31	681	47	323	46	449	44	0	.	102314	3
	broadleaves	1114	28	599	34	259	41	959	36	47	73	208	50	1092	34	1225	32	2006	22	316	33	564	40	147	56	391	44	0	.	22	100	274	53	81	66	386	35	74	100	476	44	231	61	243	42	37	70	262	41	45	76	1097	23	525	39	342	48	243	59	41	100	29	100	29	88	0	.	24	100	45	100	0	.	369	49	330	51	135	51	329	58	260	59	447	35	419	42	732	36	135	65	347	41	401	49	102	61	115	100	365	54	347	54	647	50	552	41	0	.	243	58	49	100	259	41	306	42	254	38	424	50	59	60	539	38	164	66	0	.	63	78	283	47	270	72	57	73	276	55	0	.	148	61	152	76	0	.	24	100	161	68	171	59	151	51	40	78	208	88	58	74	254	61	33	71	0	.	247	37	104	85	420	45	244	52	476	40	486	42	340	57	187	54	300	47	0	.	26917	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	.	0	0
	total	1450	27	860	30	607	38	2791	23	1113	32	1606	27	7299	13	7759	13	7755	13	2180	18	585	39	360	65	826	35	31	.	330	55	1097	32	1402	29	1400	25	74	100	2355	25	2887	21	2047	22	3185	19	5235	14	3722	16	3466	16	1790	30	2609	23	2644	24	411	40	849	31	588	43	587	49	1424	27	487	40	415	45	1469	29	1140	35	559	41	1506	28	1823	30	3184	26	1078	27	1606	32	370	44	522	36	507	53	1468	30	1310	30	1313	36	573	39	1230	38	1493	28	749	39	1430	29	907	33	351	36	366	37	333	37	984	31	333	38	941	36	625	38	411	46	592	44	1162	31	991	33	801	36	952	43	14	100	1043	34	206	62	345	59	547	32	723	42	559	36	819	36	174	63	733	53	377	74	599	54	3816	17	1943	19	3021	18	394	48	1194	34	789	34	1173	29	1180	29	1020	38	510	37	750	35	0	.	129231	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1134	29	989	37	992	40	3159	24	2837	28	4251	22	15795	11	13498	12	12269	12	6496	14	65	75	452	50	1215	43	153	.	1135	40	3102	25	3198	24	2841	24	115	81	4403	25	7091	17	4548	17	6400	16	12986	12	12665	11	6757	15	2909	26	5936	19	6979	18	814	39	2358	25	1279	34	1095	38	3116	23	1406	32	1531	32	3013	28	2415	29	1294	33	2892	24	2458	29	4588	23	2491	23	1671	33	964	34	486	43	781	32	2347	26	3342	25	1917	32	1548	32	1713	31	2161	26	1139	33	2614	24	2339	27	253	55	60	71	133	53	1485	27	756	34	799	49	1217	33	1220	42	2197	30	1772	32	1715	33	1887	32	1269	43	227	50	1897	26	565	54	881	45	2133	21	971	39	1275	28	1512	29	620	56	1167	46	508	58	567	67	9268	13	7447	14	7716	14	873	38	1304	33	1402	29	2232	26	2516	23	1284	36	1000	45	1026	33	0	.	261296	2
	broadleaves	3157	26	1654	25	875	33	2698	25	276	45	1104	32	4111	19	4216	18	6900	13	4040	15	1231	34	1201	34	1709	25	0	.	472	52	1391	31	1507	27	1700	30	645	50	2594	35	810	37	748	28	555	36	1661	22	395	40	5546	14	2033	36	1316	32	1807	27	449	41	447	31	330	39	289	47	459	31	1001	36	101	55	1550	28	1612	31	1810	34	1312	28	1528	32	2620	29	2620	21	1683	30	1234	26	1431	30	2150	26	962	35	413	62	1198	34	1442	30	1439	32	1645	26	172	49	692	33	313	60	1278	25	2068	23	3072	18	1757	24	1538	27	2289	22	834	36	85	86	217	52	1143	33	657	45	543	38	766	45	124	68	1336	26	322	61	250	45	342	35	1016	30	1269	29	826	36	1009	34	587	54	312	51	465	56	509	35	327	30	1511	21	537	44	1177	33	885	30	1568	26	1468	26	1020	34	1039	35	882	32	0	.	122287	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	.	0	.	0	0
	total	4290	23	2644	23	1867	28	5856	19	3114	28	5355	22	19906	10	17714	11	19169	10	10536	12	1296	33	1653	32	2924	27	153	.	1607	34	4493	21	4705	20	4542	20	760	48	6997	22	7901	16	5296	17	6955	15	14647	11	13060	10	12303	12	4942	22	7252	18	8786	17	1264	32	2805	24	1609	32	1384	35	3575	22	2407	27	1632	31	4564	23	4027	24	3103	26	4204	21	3985	24	7207	20	5111	18	3354	25	2199	24	1917	28	2930	25	3309	23	3756	24	3114	26	2991	25	3151	27	3806	22	1310	30	3305	21	2652	25	1531	24	2129	23	3206	17	3243	19	2294	23	3088	21	2051	28	1306	40	2414	28	2914	26	2372	28	2430	30	2035	36	351	46	3233	22	887	48	1132	40	2476	20	1987	28	2544	23	2339	25	1629	33	1754	40	821	55	1032	48	9777	12	7774	14	9227	13	1409	34	2481	28	2286	26	3801	22	3984	20	2304	28	2038	33	1908	27	0	.	383583	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. 2293441. Birmensdorf, Swiss Federal Research Institute WSL

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