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
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
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
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	76	34	119	34	99	42	93	31	1	1076	10	93	195	25	96	41	210	15	202	18	18	45	56	33	61	22	0	.	32	100	51	42	72	26	78	37	8	62	119	28	142	20	93	22	216	17	421	16	389	17	270	20	37	36	25	35	33	33	33	51	19	38	20	61	17	48	37	43	34	32	66	24	114	24	18	39	43	31	68	24	59	36	15	42	90	26	77	25	57	33	89	45	26	44	113	35	50	43	33	35	26	36	48	41	36	59	58	34	119	23	55	21	131	27	49	35	73	25	201	17	129	22	116	25	28	39	41	49	39	28	60	32	50	32	49	31	28	37	15	49	58	38	40	49	39	44	59	27	38	32	38	35	54	32	27	47	16	57	3	81	11	44	240	15	209	20	287	16	39	36	47	46	-7	328	17	42	43	35	49	60	7	43	32	44	0	.	6996	3
	16-23 cm	274	24	268	24	211	29	254	27	75	54	87	46	442	18	256	19	623	15	671	15	46	41	96	32	189	26	0	.	89	29	191	35	272	28	263	30	26	67	310	22	363	20	409	18	601	19	1129	11	1091	11	797	14	123	30	137	37	117	32	78	42	67	37	48	37	75	31	91	35	142	36	277	24	358	26	102	46	171	29	307	22	194	35	55	43	333	22	176	29	145	33	124	39	115	27	271	24	172	28	155	30	113	36	117	36	130	35	140	35	449	17	192	22	375	22	230	31	164	25	670	14	399	20	293	23	105	44	67	48	168	27	139	33	112	35	137	33	115	39	54	47	146	28	54	37	64	33	174	26	149	35	164	30	105	28	101	51	44	58	17	50	34	52	857	12	677	16	983	13	96	35	112	39	111	41	44	59	117	46	139	39	73	34	191	36	0	.	21488	2
	24-35 cm	468	25	483	28	554	24	527	25	183	35	294	41	1086	16	839	18	1211	15	1907	15	177	38	299	29	470	22	1	.	327	39	543	30	864	22	574	32	97	62	812	26	1167	16	921	16	1025	16	2898	10	2705	9	1838	13	238	30	480	36	271	40	161	36	147	34	292	35	168	32	212	29	453	33	573	25	907	21	105	47	414	27	814	21	393	33	276	34	956	19	430	27	447	25	248	29	477	29	560	24	471	28	333	33	278	43	205	35	254	32	155	28	767	20	508	24	547	20	237	34	370	33	844	16	566	21	669	21	331	36	69	45	433	29	344	28	279	30	325	31	308	42	93	62	567	26	137	46	289	37	519	25	306	31	438	28	400	29	145	37	123	56	133	50	121	49	1720	13	1526	14	2130	12	279	33	281	42	173	36	146	46	293	35	291	33	233	41	314	29	0	.	50239	2
	36-51 cm	819	20	585	26	762	23	1262	18	438	37	650	36	1953	17	1476	16	1972	14	3061	13	350	37	559	28	883	22	60	.	464	38	818	26	1252	22	1000	26	75	59	1788	19	2158	15	1917	15	1801	16	5076	10	4945	9	4063	12	462	27	559	31	334	32	403	30	282	32	488	30	349	31	546	28	448	31	737	23	1059	19	330	42	703	26	1549	18	743	25	390	31	1568	18	668	20	1150	23	685	25	908	21	962	21	644	25	802	24	144	61	335	36	535	29	281	34	976	17	913	21	431	21	217	30	301	32	1069	15	407	24	576	24	344	33	148	35	599	30	588	34	535	30	579	32	339	32	129	54	791	23	347	37	416	31	954	22	568	25	1022	20	759	23	344	51	372	40	211	42	130	42	2619	11	2106	14	3021	12	413	29	397	31	378	31	126	43	499	25	307	34	434	38	447	28	0	.	83062	2
	=52 cm	950	23	954	28	884	24	2023	20	347	49	536	37	2431	19	2525	19	3386	15	2314	17	347	39	651	37	755	32	40	.	467	38	610	34	1301	25	1237	30	19	100	2111	19	2518	20	2568	17	2986	15	5488	11	4629	15	2645	14	802	34	590	43	249	35	514	34	389	46	378	40	512	35	877	28	175	53	767	29	1349	23	433	39	441	29	1517	24	1231	28	1163	33	1158	22	1418	25	391	30	658	29	432	28	1420	25	689	27	1381	29	55	52	305	52	791	40	789	26	1795	19	989	23	492	46	208	49	227	40	1247	16	444	31	688	29	268	47	451	44	650	41	651	32	1107	28	633	33	493	48	95	89	962	30	473	41	382	38	592	25	866	29	757	24	703	32	128	48	411	39	431	50	518	42	3963	13	1887	16	2971	16	502	35	936	32	763	35	262	44	479	34	427	51	299	40	633	33	0	.	95385	2
	total	2587	16	2408	20	2510	18	4159	16	1044	34	1577	33	6106	14	5193	14	7401	11	8154	11	938	30	1661	25	2358	21	101	.	1380	30	2213	24	3761	19	3152	21	225	55	5140	16	6348	13	5908	13	6630	12	15013	8	13759	9	9612	10	1663	24	1791	29	1004	29	1189	26	905	31	1225	28	1121	28	1763	24	1252	28	2420	20	3787	17	988	32	1772	23	4256	17	2619	22	1900	29	4105	15	2769	19	2189	20	1803	21	1958	20	3327	18	2026	21	2704	22	616	37	1011	30	1746	26	1422	21	4106	14	2658	18	1977	19	942	25	1135	23	4031	12	1945	17	2343	18	1076	31	775	35	1887	25	1783	25	2082	23	1723	27	1282	29	386	49	2524	21	1051	34	1190	26	2299	18	1927	22	2419	19	2021	21	746	39	966	32	796	42	814	34	9399	9	6405	12	9392	10	1330	26	1773	25	1417	26	595	36	1432	24	1212	29	1046	31	1617	23	0	.	257169	1
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	14	111	15	48	2	100	9	46	31	37	32	32	357	13	216	17	150	23	59	35	4	83	7	73	8	48	0	.	4	64	32	38	39	30	81	61	8	63	18	34	50	28	23	40	107	36	104	63	16	54	84	22	83	27	120	26	114	19	11	75	44	30	1	100	10	56	33	44	13	39	13	.	20	37	38	28	20	63	38	45	21	49	64	29	17	61	3	61	14	54	4	60	20	37	44	45	17	105	9	149	40	39	75	51	67	57	42	64	1	.	29	58	20	86	59	31	150	23	31	42	43	51	55	33	12	43	5	62	20	51	10	44	28	47	90	34	16	51	8	88	16	41	5	71	4	.	24	48	11	62	4	259	11	47	7	53	2	80	0	.	23	63	109	27	74	23	75	31	17	44	21	64	22	44	88	47	29	37	7	57	26	69	21	73	0	.	3635	5
	16-23 cm	56	34	28	43	14	51	85	79	78	34	188	25	951	11	750	15	630	18	162	28	9	90	29	53	51	48	0	.	45	55	141	28	199	31	178	54	19	59	64	54	130	28	109	38	350	30	141	29	99	38	248	21	259	25	253	27	353	17	17	77	132	32	6	71	21	63	203	45	102	41	14	.	61	41	184	30	82	62	106	36	80	52	208	24	44	42	14	92	61	50	14	74	90	32	130	37	92	37	70	53	141	27	76	45	286	42	37	54	4	.	52	50	111	44	234	30	469	21	122	47	137	42	212	32	75	39	42	51	49	47	64	37	71	46	219	28	51	56	31	86	100	47	41	80	23	.	92	48	42	53	23	61	56	43	45	67	41	81	0	.	29	61	287	22	294	21	263	28	85	44	28	51	61	35	211	30	113	31	42	70	72	45	19	73	0	.	11801	4
	24-35 cm	270	28	86	44	179	72	227	54	328	34	630	25	3028	10	1877	13	1433	16	555	30	20	60	35	61	249	43	0	.	102	43	458	34	519	29	458	31	37	58	227	37	382	38	263	37	431	25	470	26	260	40	846	20	526	24	576	23	1121	19	99	84	420	33	41	82	45	85	365	29	217	49	51	.	179	36	597	26	248	46	224	38	265	38	811	22	177	49	50	79	69	66	40	82	352	37	197	39	231	35	155	45	655	26	287	37	330	34	86	62	22	.	73	51	113	46	491	24	752	19	225	46	238	42	298	30	265	43	240	39	68	73	149	43	162	51	491	35	182	46	16	100	431	48	66	74	31	.	174	49	148	46	32	75	160	43	94	48	60	82	0	.	160	45	616	23	573	21	458	24	85	49	89	61	145	37	523	25	506	25	131	49	66	49	44	59	0	.	30161	3
	36-51 cm	927	25	145	39	183	52	371	41	802	29	1570	23	6201	9	4274	11	3038	13	1175	24	81	56	217	55	306	36	0	.	251	51	813	29	932	25	751	27	176	54	440	32	945	25	355	36	676	26	628	24	249	42	1748	17	1350	26	1259	20	2150	16	90	54	622	26	216	68	121	63	920	25	252	35	13	.	350	47	1110	23	456	41	296	32	445	37	2032	18	324	42	186	53	198	46	83	72	612	29	306	39	538	40	287	45	952	26	425	40	494	32	56	58	4	.	127	59	72	69	523	24	654	22	117	39	273	37	317	32	255	39	334	49	147	53	409	37	142	46	851	25	186	49	116	59	331	40	232	46	6	.	345	33	176	54	366	50	285	39	211	48	178	58	0	.	210	44	998	21	573	25	585	25	382	37	454	42	410	34	1000	22	937	26	358	40	302	52	168	53	0	.	56432	3
	=52 cm	689	30	269	40	114	66	568	39	549	39	2043	25	6868	11	6338	12	3688	15	800	32	335	55	158	60	469	43	0	.	169	61	608	36	835	27	703	32	75	74	399	39	1109	23	412	55	775	27	902	25	223	51	1264	22	1335	29	1568	23	2438	20	151	62	670	29	159	71	213	66	1103	30	387	38	18	.	494	44	1017	30	320	50	495	38	627	31	2818	20	210	56	348	49	109	80	86	79	496	43	532	50	480	35	170	64	888	30	502	51	441	38	44	73	44	.	76	75	66	86	550	44	276	32	390	44	161	55	335	37	234	45	77	62	60	60	470	41	243	55	1225	28	307	46	24	71	166	77	342	50	0	.	105	52	176	58	156	51	321	43	55	50	273	52	0	.	328	55	1423	23	699	28	560	28	420	40	364	47	432	36	1200	26	641	35	325	52	355	49	494	43	0	.	61855	3
	total	1956	24	544	35	492	52	1260	36	1788	28	4463	21	17405	8	13454	10	8939	12	2751	22	449	50	446	50	1083	34	0	.	571	46	2052	26	2523	21	2171	26	314	56	1148	31	2616	20	1163	33	2339	21	2245	21	846	33	4189	16	3553	20	3776	18	6176	15	368	52	1888	23	423	66	410	49	2624	25	971	32	110	.	1103	40	2946	21	1125	35	1160	28	1437	29	5932	17	772	38	602	45	451	44	228	70	1570	28	1209	33	1358	32	691	38	2676	23	1364	33	1618	25	265	53	75	.	358	42	382	42	1857	22	2302	16	884	33	852	33	1217	26	840	35	699	42	345	53	1102	33	647	36	2876	23	741	43	194	60	1045	41	687	43	64	.	740	32	553	44	581	46	834	38	412	43	554	52	0	.	749	44	3433	18	2213	20	1940	21	988	34	957	40	1070	31	3021	19	2226	23	864	38	820	37	747	45	0	.	163884	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	.
	12-15 cm	90	33	134	31	100	42	102	28	32	55	42	33	551	12	312	17	360	13	262	16	22	40	63	30	69	21	0	.	36	88	82	30	111	20	159	36	16	44	137	25	192	16	116	19	323	16	525	18	405	16	354	16	120	21	144	22	147	16	44	43	63	23	21	58	27	37	69	31	47	26	78	26	133	21	55	23	62	29	107	22	80	30	79	25	106	23	80	24	70	28	93	43	46	30	157	28	67	42	42	42	66	28	123	35	103	42	100	34	121	23	84	24	151	26	108	23	223	17	232	15	173	21	171	20	40	30	46	44	59	25	70	28	78	26	139	24	43	30	22	44	75	31	45	44	43	41	83	24	50	28	42	40	65	27	34	39	18	52	3	81	34	45	348	13	284	16	362	15	56	28	69	37	15	182	105	40	72	26	56	53	33	56	53	39	0	.	10631	3
	16-23 cm	329	21	296	22	225	28	338	28	153	31	275	22	1392	9	1006	12	1253	12	833	13	56	38	125	28	240	23	0	.	134	27	333	24	471	20	441	28	45	46	374	21	494	17	517	16	951	16	1270	10	1191	10	1044	12	382	20	391	22	470	15	95	37	199	24	54	33	95	28	294	33	244	27	291	23	420	23	286	25	252	28	413	18	274	29	262	21	377	20	190	28	206	27	138	36	206	21	401	20	265	22	224	27	254	22	194	28	416	31	177	30	453	17	245	20	486	20	464	21	633	17	792	14	536	18	506	19	179	30	109	35	217	23	203	25	183	28	356	22	166	32	85	43	246	26	96	40	88	36	266	24	191	30	187	27	161	24	146	41	85	49	17	50	63	39	1145	11	970	13	1246	12	181	28	140	33	171	29	255	26	229	28	181	34	145	28	210	33	0	.	33289	2
	24-35 cm	737	19	569	24	733	25	754	24	511	25	924	21	4114	9	2716	10	2644	11	2461	13	196	35	335	27	719	21	1	.	429	31	1001	22	1383	17	1032	23	134	48	1039	22	1549	15	1185	15	1456	14	3368	9	2965	9	2684	11	764	19	1056	21	1391	17	260	39	567	26	332	32	213	31	578	21	671	27	624	25	1085	19	703	23	662	24	1039	18	657	25	1087	19	1133	17	481	25	516	23	288	27	829	23	758	20	702	22	489	27	933	22	491	26	584	24	240	29	788	20	581	22	661	18	728	19	1122	16	1069	16	804	19	968	17	596	28	309	32	501	27	493	24	441	27	816	24	490	31	109	55	998	25	203	39	320	34	693	22	453	25	469	26	560	24	239	29	183	46	133	50	280	33	2336	11	2099	12	2588	11	364	28	371	35	318	26	669	22	799	20	422	27	298	33	358	27	0	.	80400	2
	36-51 cm	1746	16	730	22	945	21	1633	16	1240	23	2221	19	8154	8	5750	9	5010	9	4236	11	431	32	776	25	1189	19	60	.	715	30	1631	19	2184	16	1752	19	251	42	2228	16	3103	13	2272	13	2477	14	5704	9	5194	9	5810	9	1812	21	1818	17	2484	15	493	26	905	20	703	29	471	28	1466	19	699	24	750	23	1409	18	1440	20	1159	23	1845	16	1188	21	2422	16	1893	16	854	20	1348	20	768	24	1520	17	1268	19	1182	23	1089	21	1096	24	760	27	1029	21	338	29	979	17	1041	20	503	20	741	19	955	18	1185	14	680	20	892	19	599	25	482	36	746	26	998	25	677	25	1429	20	525	27	245	40	1121	20	579	29	421	31	1299	18	743	23	1389	20	1044	20	556	36	550	33	211	42	341	32	3617	10	2679	12	3606	11	795	23	850	27	787	23	1126	20	1436	19	665	26	736	31	615	25	0	.	139493	1
	=52 cm	1639	18	1223	23	999	22	2591	18	896	30	2579	22	9299	9	8863	10	7073	10	3114	15	682	33	809	32	1224	26	40	.	637	32	1218	25	2136	18	1941	22	94	62	2510	17	3627	15	2980	17	3761	13	6390	10	4852	15	3909	12	2138	22	2158	20	2687	18	665	30	1059	25	538	35	725	31	1980	21	562	31	786	29	1843	20	1451	24	762	27	2012	20	1858	21	3981	17	1367	21	1766	23	500	29	744	27	928	26	1952	23	1169	21	1551	27	943	28	807	37	1232	29	832	25	1839	19	1065	22	558	42	758	35	504	25	1638	16	605	27	1023	23	502	33	527	39	710	38	1121	25	1350	25	1859	22	799	34	119	72	1128	28	815	32	382	38	698	23	1043	26	912	22	1025	25	183	37	684	31	431	50	845	33	5386	11	2586	14	3531	14	922	26	1300	27	1195	25	1462	23	1121	25	753	36	654	32	1127	26	0	.	157240	2
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2293610. Birmensdorf, Swiss Federal Research Institute WSL

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