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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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
growing stock (stemwood)
ownership (2 categories) · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 m³
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
ownership (2 categories)	diameter at breast height (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
public	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	.
	12-15 cm	62	50	76	65	142	50	44	32	-16	122	16	103	157	42	36	106	233	20	221	21	27	52	47	49	73	26	0	.	64	84	63	54	66	38	25	50	11	77	88	26	158	28	81	24	206	24	476	23	381	21	183	20	46	46	28	37	42	39	53	47	26	48	17	98	25	55	59	48	35	48	70	34	87	38	27	45	34	44	82	27	72	48	25	45	110	31	84	33	68	42	71	71	16	99	81	33	58	69	18	47	30	51	48	54	12	57	73	46	111	37	59	28	111	31	18	58	62	36	209	23	94	32	122	28	46	41	66	55	39	37	81	40	38	42	40	55	15	53	2	100	60	44	24	61	51	55	64	34	33	43	51	45	31	41	41	54	22	70	0	.	3	100	212	21	232	29	298	26	25	50	26	54	-22	181	25	50	35	40	26	37	7	53	52	49	0	.	6728	4
	16-23 cm	233	32	182	39	202	44	167	33	22	40	149	45	337	23	238	25	618	21	745	20	64	50	69	47	252	33	0	.	65	43	242	40	363	31	189	46	19	77	285	29	355	30	287	24	380	23	1112	14	1161	14	705	19	129	33	112	39	151	36	98	49	99	43	74	40	95	39	84	51	172	52	224	31	276	30	108	52	160	35	325	27	287	40	77	52	405	28	159	36	182	41	79	54	120	35	242	34	191	42	108	56	158	44	120	48	101	53	173	50	423	25	158	31	397	29	239	48	144	36	717	20	403	28	280	32	158	51	112	52	233	33	106	45	83	41	70	62	81	80	47	60	167	31	53	53	56	44	227	32	187	44	224	37	60	45	174	55	67	67	8	100	11	100	808	19	679	25	726	20	106	46	115	48	93	71	49	73	133	68	190	47	86	42	221	44	0	.	21041	3
	24-35 cm	400	30	471	40	442	35	336	31	238	42	482	43	1130	21	782	23	1157	20	2208	19	147	55	294	41	520	29	2	.	398	52	631	36	741	30	478	42	160	66	715	41	1180	21	689	23	703	25	2829	14	2859	13	1421	18	300	34	616	44	398	45	141	53	196	38	212	40	192	44	200	39	567	41	457	35	937	28	132	55	376	39	722	30	354	42	359	41	1005	25	424	34	510	33	242	41	386	42	386	30	576	36	319	38	276	50	203	40	284	45	95	46	656	28	522	32	442	29	237	55	266	55	911	21	665	26	559	29	417	47	95	50	570	36	356	39	247	35	197	52	215	82	127	76	794	31	92	73	133	50	617	32	321	34	466	37	243	47	236	41	89	54	192	62	16	100	1550	17	1429	21	1732	17	320	43	204	42	168	49	246	48	272	49	218	43	355	45	324	35	0	.	48777	3
	36-51 cm	667	30	382	31	668	34	1181	25	668	40	900	41	2220	23	1565	21	2132	17	3313	16	260	58	543	39	821	31	106	.	568	46	948	32	912	33	920	35	76	71	1818	25	2267	18	1469	20	1706	25	4542	13	5040	12	3781	17	503	32	725	31	482	37	299	44	335	41	444	39	407	40	558	38	504	43	478	32	1097	26	492	46	577	37	1215	24	641	31	339	38	1780	23	606	29	1111	29	704	33	750	31	986	29	651	33	895	30	178	79	315	49	553	34	318	46	871	22	1088	29	299	30	151	41	211	54	905	21	495	32	539	32	350	45	166	43	823	35	620	45	618	40	267	46	208	49	158	72	864	27	161	62	327	44	771	26	491	36	1135	27	702	29	506	59	354	50	251	55	15	136	2126	17	2089	18	2765	17	335	43	323	39	434	42	122	58	406	34	318	45	616	42	442	36	0	.	80740	3
	=52 cm	745	35	680	39	680	37	2237	26	553	52	705	45	2295	26	2462	25	3739	19	2191	19	338	47	414	64	929	43	70	.	274	56	752	42	1005	40	779	40	0	.	2339	25	2114	27	2172	22	2677	22	5309	14	3948	16	2645	19	819	44	833	48	376	38	389	56	530	57	439	53	489	46	704	40	204	72	480	46	1061	29	592	43	349	40	1137	36	1706	33	1180	44	1139	28	1188	38	388	45	671	42	342	43	1811	29	595	39	1044	40	79	59	457	57	1123	45	930	35	1581	29	981	33	421	33	84	58	100	84	913	26	394	35	677	44	360	57	398	54	565	52	805	37	1014	36	512	55	281	62	0	.	1013	37	203	73	377	58	635	32	734	40	548	36	912	40	203	51	309	46	433	68	378	71	3029	19	1621	22	3267	18	223	52	999	42	377	46	213	72	497	48	504	65	432	44	571	44	0	.	89671	3
	total	2108	23	1790	26	2133	26	3966	21	1465	40	2252	37	6139	19	5082	19	7879	14	8679	14	836	39	1367	33	2594	28	177	.	1369	39	2636	28	3087	25	2390	30	265	65	5245	21	6075	17	4698	18	5671	18	14269	11	13390	10	8734	14	1796	30	2315	33	1448	33	980	40	1185	39	1186	37	1208	37	1604	31	1482	37	1710	32	3458	22	1351	37	1497	32	3481	24	3061	28	1980	37	4439	19	2461	27	2258	25	1768	30	1613	30	3507	24	2071	29	2384	30	722	47	1143	39	2074	33	1589	29	3642	20	2809	25	1671	22	730	39	784	36	3656	16	2051	23	2177	26	1329	39	837	40	2231	31	1967	31	2000	31	1086	45	800	47	333	60	2898	24	533	60	944	36	2313	23	1767	30	2424	25	1948	29	1161	44	841	39	883	55	423	70	7725	13	6051	16	8788	14	1009	37	1667	34	1049	36	655	49	1343	33	1257	38	1497	35	1610	29	0	.	246957	2
private	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	.
	12-15 cm	-11	217	15	72	0	.	14	50	14	52	33	43	409	17	180	18	168	33	41	39	0	100	8	100	8	71	0	.	4	73	46	42	48	37	73	85	3	71	13	43	41	43	38	44	94	35	15	38	23	66	57	33	89	36	133	30	127	24	20	76	44	40	2	100	9	85	15	52	17	39	21	.	18	49	15	54	32	69	49	57	6	71	53	31	20	80	6	61	0	.	6	73	12	60	44	70	13	238	13	180	52	49	126	52	42	43	17	71	0	.	3	71	44	65	86	38	174	31	10	77	46	69	79	39	20	45	6	85	22	71	6	71	44	51	74	47	19	66	13	88	12	46	1	100	0	.	14	54	12	90	13	86	11	70	7	72	2	100	0	.	30	81	126	39	108	28	76	31	12	65	37	63	23	42	40	32	22	39	6	83	35	90	10	85	0	.	3577	6
	16-23 cm	34	53	13	79	0	.	137	81	70	44	149	36	910	14	717	21	709	21	171	39	9	71	33	77	33	62	0	.	48	77	195	33	241	38	242	65	24	71	65	65	80	34	165	44	303	38	85	43	107	52	174	32	250	36	302	32	396	21	31	76	140	46	5	100	14	100	140	47	151	47	24	.	76	52	90	50	144	62	116	48	75	91	128	35	59	50	24	92	7	.	25	74	80	46	79	49	90	58	123	55	120	37	78	58	309	41	33	74	0	.	21	117	126	63	314	37	629	26	78	96	105	73	272	40	88	45	71	55	44	60	72	51	56	49	210	41	84	59	55	86	78	54	0	.	0	.	47	53	41	82	20	100	57	59	79	69	57	95	0	.	19	77	337	32	398	26	346	36	61	71	38	59	49	58	205	39	73	49	53	95	23	72	21	100	0	.	12050	5
	24-35 cm	113	51	69	65	0	.	276	65	343	47	546	35	3118	14	1836	17	1521	20	684	40	14	100	51	73	138	75	0	.	73	73	542	44	421	39	344	35	43	72	272	43	185	52	398	42	494	33	383	45	190	45	587	31	491	30	595	31	1229	22	181	83	445	48	15	100	84	83	293	41	387	47	85	.	248	40	391	37	340	52	296	49	265	49	720	28	278	55	66	100	13	.	70	82	330	54	143	47	237	48	249	49	823	34	363	46	362	43	23	100	0	.	95	65	104	58	568	30	789	24	127	100	245	63	339	38	345	52	267	52	37	73	186	48	178	71	437	57	231	57	29	100	245	44	16	100	0	.	102	68	171	61	62	69	208	54	144	53	99	82	0	.	123	63	661	29	632	28	451	28	31	140	127	73	127	48	670	31	608	30	157	63	49	85	44	71	0	.	30365	4
	36-51 cm	691	34	155	46	0	.	530	44	750	41	1408	30	6437	12	3528	15	3438	16	1061	32	17	158	157	100	248	48	0	.	120	81	954	35	844	34	866	30	209	72	504	40	1212	30	389	40	856	31	589	35	196	51	1549	24	1570	37	1561	24	2401	21	165	55	663	35	148	76	134	82	858	33	364	40	22	.	452	57	781	36	754	42	280	41	484	54	2119	22	243	54	156	75	90	.	146	72	437	40	333	54	415	44	348	56	703	31	484	45	510	35	31	71	0	.	127	75	17	100	481	30	688	29	21	100	115	50	405	39	324	44	526	56	172	69	376	46	185	55	518	42	265	56	206	59	236	51	155	85	0	.	332	46	95	69	276	68	279	55	192	59	181	81	0	.	225	58	1108	31	575	28	673	34	303	54	357	59	417	42	1135	26	1347	29	335	46	269	73	72	83	0	.	56345	4
	=52 cm	526	37	199	49	0	.	927	40	721	45	1254	36	6817	14	5608	15	4244	18	587	38	169	88	0	.	462	49	0	.	47	100	631	47	988	35	757	30	87	100	352	53	1446	28	293	45	989	33	649	34	276	67	1182	29	1245	37	1854	26	2613	26	278	62	438	44	149	100	63	89	743	34	489	43	31	.	576	56	757	56	345	52	672	45	551	47	3026	25	130	77	400	63	0	.	152	79	135	62	65	72	492	46	287	64	636	41	824	53	588	45	80	73	0	.	139	75	19	100	367	38	425	36	221	82	67	100	444	47	363	48	141	62	87	71	305	58	405	59	689	44	376	58	42	71	71	58	191	80	0	.	56	58	97	100	112	72	230	56	49	71	217	78	0	.	279	64	1293	33	712	35	514	39	409	59	319	72	250	49	1179	33	508	44	477	58	85	72	202	71	0	.	58168	4
	total	1354	33	451	43	0	.	1883	38	1897	39	3390	28	17690	11	11869	13	10080	14	2545	28	209	79	248	87	888	46	0	.	293	76	2369	31	2542	28	2281	28	367	75	1206	39	2964	25	1283	37	2736	26	1721	30	791	45	3549	22	3645	27	4445	21	6767	20	676	51	1730	33	319	74	304	70	2048	31	1407	36	182	.	1370	48	2033	34	1615	37	1412	35	1381	44	6046	20	730	48	652	58	110	.	398	70	994	41	663	46	1248	39	1019	42	2334	29	1876	38	1811	29	184	71	0	.	386	58	309	61	1816	27	2706	19	457	62	577	52	1538	33	1140	40	1011	51	361	67	945	43	869	42	1928	36	976	52	345	60	643	43	363	71	0	.	550	44	414	60	484	58	784	51	472	52	556	72	0	.	676	58	3524	26	2425	23	2060	27	817	54	878	54	866	37	3229	24	2557	27	1027	46	461	64	350	71	0	.	160505	3
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	.
	12-15 cm	51	78	90	56	142	50	58	27	-1	1697	49	44	566	17	217	23	401	18	262	19	26	53	55	44	80	24	0	.	67	80	109	36	114	27	97	65	14	61	101	23	199	24	119	21	300	20	492	23	404	20	240	17	136	28	161	25	169	20	73	40	70	31	19	87	34	46	74	40	52	34	91	35	105	32	43	35	66	40	131	27	78	44	78	26	130	29	90	31	68	42	76	67	28	62	125	32	71	72	31	81	82	36	174	41	55	35	90	40	111	37	62	27	155	29	105	33	236	25	219	23	140	31	201	23	66	31	72	51	61	35	87	37	82	34	114	36	34	44	15	78	72	37	25	59	51	55	77	29	45	39	64	40	42	35	48	47	24	65	0	.	34	74	338	20	340	22	374	21	37	40	63	43	0	14262	65	27	57	29	32	33	42	76	62	43	0	.	10305	3
	16-23 cm	267	29	195	37	202	44	304	41	91	35	298	29	1246	12	955	17	1327	15	916	17	73	45	102	40	285	30	0	.	113	41	437	26	604	24	431	42	43	52	350	27	436	25	451	22	683	21	1197	13	1268	14	879	16	378	26	414	26	548	18	129	41	238	32	79	38	110	36	224	35	322	35	248	30	352	26	198	36	304	35	441	24	363	37	205	29	464	26	183	34	189	40	105	45	199	28	321	29	281	34	230	39	279	29	198	37	411	34	206	44	423	25	179	30	523	26	553	30	773	22	795	20	508	26	551	25	245	36	183	38	277	29	178	33	139	31	280	34	165	49	102	54	246	27	53	53	56	44	274	28	227	39	243	35	116	37	253	43	123	57	8	100	30	61	1145	16	1077	18	1072	18	168	39	153	39	142	51	254	34	206	47	243	42	109	36	243	41	0	.	33091	2
	24-35 cm	514	26	540	35	442	35	612	34	580	32	1028	27	4248	12	2618	14	2677	14	2892	17	161	51	344	36	658	28	2	.	471	45	1174	28	1163	24	822	29	204	54	987	32	1365	20	1088	21	1197	20	3212	13	3049	12	2008	15	791	23	1211	27	1627	20	322	52	641	35	227	38	276	39	493	29	954	31	541	33	1185	23	523	31	717	32	1017	25	618	32	1079	23	1282	22	490	32	523	33	312	37	716	33	529	25	813	29	568	30	1099	28	566	33	647	31	118	42	656	28	617	29	546	26	806	27	1056	22	1038	22	910	25	898	23	762	35	362	40	607	34	541	31	425	36	634	43	446	49	155	65	1039	26	109	64	133	50	718	29	492	30	528	34	451	36	381	32	189	50	192	62	139	57	2211	15	2062	17	2183	15	350	42	331	38	295	35	916	26	880	25	375	36	404	40	368	32	0	.	79141	2
	36-51 cm	1358	22	537	26	668	34	1711	22	1417	29	2308	24	8657	11	5093	12	5569	12	4374	14	277	55	700	38	1069	26	106	.	688	41	1902	23	1755	24	1786	23	285	56	2321	21	3479	15	1858	18	2562	20	5131	12	5236	12	5331	13	2073	29	2286	19	2883	19	465	34	998	27	592	35	541	36	1415	25	868	30	500	31	1550	25	1273	28	1331	29	1495	21	1126	29	2458	20	2023	21	762	27	1201	28	850	30	1187	24	1320	25	1067	27	1243	27	881	29	798	33	1063	24	349	42	871	22	1216	27	316	29	631	25	899	25	927	21	610	27	944	25	674	32	692	44	995	31	997	33	804	33	786	32	473	38	364	45	1100	24	316	52	327	44	1103	23	586	32	1412	25	981	26	699	45	535	43	251	55	240	55	3234	15	2664	15	3438	15	638	34	679	36	851	29	1257	24	1753	24	653	32	886	37	514	33	0	.	137086	2
	=52 cm	1271	26	879	32	680	37	3164	21	1274	34	1959	28	9112	12	8069	13	7984	13	2779	17	507	43	414	64	1391	33	70	.	322	50	1383	31	1993	26	1535	25	87	100	2692	23	3560	19	2464	20	3666	18	5958	13	4224	16	3827	15	2063	28	2687	23	2989	24	668	42	968	37	588	47	552	42	1448	25	693	37	511	44	1636	27	1349	37	694	33	1809	28	2257	28	4205	22	1269	26	1588	32	388	45	823	37	477	35	1876	28	1087	30	1331	34	715	37	1282	40	1711	33	1010	33	1581	29	1121	30	440	32	452	32	526	33	1134	26	461	33	1121	32	722	37	539	43	651	46	1110	31	1419	30	1201	35	657	42	42	71	1083	35	394	54	377	58	690	30	831	37	660	32	1142	34	252	44	526	42	433	68	657	49	4322	16	2333	18	3781	17	632	42	1319	36	627	34	1392	30	1005	32	981	44	517	39	773	37	0	.	147839	2
	total	3462	19	2241	23	2133	26	5849	19	3362	28	5641	22	23829	9	16952	10	17958	10	11223	12	1046	35	1615	31	3482	24	177	.	1662	34	5005	21	5629	18	4671	20	632	51	6452	18	9039	14	5981	16	8408	15	15991	10	14180	10	12284	12	5441	20	6759	18	8215	17	1656	32	2915	24	1505	33	1512	32	3653	22	2890	26	1892	30	4828	21	3385	25	3112	25	4893	20	4442	23	8026	18	5169	18	3113	24	2368	25	2166	28	2607	24	4170	21	3319	23	3403	24	3056	25	3018	28	3885	22	1773	27	3642	20	3195	23	1980	21	2546	22	3490	17	4113	16	2628	21	3715	20	2469	28	1847	33	2591	28	2913	25	2870	25	3015	28	1776	36	679	42	3540	21	896	46	944	36	2863	20	2181	27	2908	23	2732	25	1633	35	1397	37	883	55	1099	45	11249	12	8475	13	10848	12	1826	31	2545	29	1915	26	3884	21	3900	21	2284	29	1958	30	1960	27	0	.	407462	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. 2293602. Birmensdorf, Swiss Federal Research Institute WSL

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