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 NFI1-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	145	45	36	106	233	20	216	21	27	52	47	49	62	28	0	.	64	84	62	55	58	42	25	50	3	100	65	27	122	33	72	26	130	31	440	25	307	25	183	20	46	46	28	37	42	39	53	47	26	48	17	98	25	55	59	48	28	54	68	35	82	40	27	45	28	51	65	31	72	48	25	45	110	31	84	33	68	42	71	71	16	99	51	39	58	69	18	47	30	51	48	54	12	57	70	48	87	45	45	31	107	33	14	70	61	36	169	28	88	34	95	33	44	42	30	72	39	37	77	41	35	44	40	55	15	53	2	100	60	44	24	61	51	55	62	34	33	43	51	45	31	41	34	62	22	70	0	.	3	100	200	23	224	30	277	27	25	50	26	54	-22	181	16	53	35	40	26	37	7	53	52	49	0	.	6135	5
	16-23 cm	233	32	182	39	202	44	167	33	22	40	149	45	320	24	238	25	618	21	739	20	64	50	69	47	190	37	0	.	65	43	224	42	315	32	189	46	19	77	258	32	312	33	233	24	234	31	1003	15	962	15	705	19	124	34	112	39	151	36	98	49	99	43	74	40	95	39	84	51	172	52	220	32	274	31	108	52	140	38	275	31	287	40	77	52	405	28	159	36	182	41	79	54	120	35	191	40	188	43	108	56	158	44	120	48	101	53	161	54	387	27	153	31	352	31	156	59	142	36	562	24	380	29	216	39	175	45	55	76	221	34	106	45	77	43	70	62	81	80	47	60	167	31	53	53	56	44	223	33	180	45	224	37	60	45	158	60	67	67	8	100	11	100	722	20	649	26	684	21	106	46	104	52	93	71	47	76	133	68	190	47	86	42	221	44	0	.	19499	3
	24-35 cm	400	30	471	40	442	35	336	31	238	42	482	43	1101	22	782	23	1157	20	2199	19	147	55	294	41	485	31	2	.	398	52	618	37	685	32	478	42	150	71	704	42	1128	22	645	24	586	28	2614	15	2647	13	1421	18	300	34	616	44	398	45	141	53	196	38	212	40	192	44	200	39	567	41	442	36	921	28	132	55	305	42	626	33	354	42	359	41	1005	25	424	34	510	33	242	41	386	42	313	33	556	37	319	38	276	50	203	40	284	45	62	46	672	27	509	33	417	30	233	57	274	54	766	24	632	27	478	32	410	48	60	54	570	36	321	42	247	35	197	52	215	82	127	76	794	31	92	73	133	50	617	32	321	34	454	38	243	47	205	45	89	54	192	62	16	100	1433	18	1384	21	1690	18	214	42	204	42	168	49	212	54	272	49	218	43	355	45	324	35	0	.	46907	3
	36-51 cm	667	30	382	31	668	34	1181	25	668	40	900	41	2198	23	1565	21	2132	17	3252	16	260	58	543	39	746	33	106	.	568	46	852	34	895	33	920	35	76	71	1638	27	2103	19	1264	21	1637	26	4264	14	4918	13	3781	17	503	32	725	31	482	37	299	44	335	41	444	39	407	40	558	38	504	43	469	33	1069	27	492	46	515	40	1139	25	641	31	339	38	1780	23	606	29	1111	29	704	33	750	31	846	31	626	35	895	30	178	79	315	49	515	36	299	48	870	22	1073	29	257	34	121	44	211	54	779	23	480	32	506	34	350	45	132	48	823	35	613	45	568	43	267	46	208	49	158	72	864	27	161	62	327	44	727	27	474	37	1114	27	702	29	506	59	354	50	251	55	15	136	2056	17	1961	19	2685	18	264	47	323	39	434	42	122	58	406	34	318	45	616	42	442	36	0	.	78261	3
	=52 cm	745	35	680	39	680	37	2237	26	553	52	705	45	2205	26	2462	25	3739	19	1882	20	338	47	414	64	575	42	70	.	274	56	752	42	1005	40	779	40	0	.	2287	25	1986	28	1963	23	2473	23	4912	15	3895	16	2645	19	819	44	833	48	376	38	389	56	530	57	439	53	489	46	704	40	204	72	454	49	1013	30	592	43	242	49	1124	36	1706	33	1180	44	1139	28	1188	38	388	45	671	42	342	43	1745	30	595	39	1044	40	79	59	457	57	723	44	782	40	1536	30	908	35	360	34	84	58	100	84	831	28	386	36	636	46	360	57	398	54	565	52	735	40	952	38	512	55	281	62	0	.	1013	37	203	73	377	58	635	32	716	41	498	38	912	40	203	51	309	46	433	68	378	71	2959	19	1546	23	3106	19	194	58	999	42	377	46	193	79	497	48	504	65	432	44	571	44	0	.	86200	3
	total	2108	23	1790	26	2133	26	3966	21	1465	40	2252	37	5970	20	5082	19	7879	14	8288	14	836	39	1367	33	2058	30	177	.	1369	39	2507	29	2958	26	2390	30	247	69	4952	22	5650	18	4177	19	5060	19	13233	11	12728	11	8734	14	1792	30	2315	33	1448	33	980	40	1185	39	1186	37	1208	37	1604	31	1476	37	1653	33	3359	22	1351	37	1231	36	3229	25	3061	28	1980	37	4439	19	2461	27	2258	25	1768	30	1613	30	3147	25	2022	30	2384	30	722	47	1143	39	1637	33	1373	33	3551	21	2687	26	1493	24	608	43	788	36	3106	18	1966	23	1931	28	1339	39	675	47	2218	31	1851	33	1879	33	1086	45	800	47	333	60	2898	24	533	60	944	36	2264	23	1724	31	2340	26	1948	29	1106	46	841	39	883	55	423	70	7369	13	5764	16	8443	14	803	39	1655	34	1049	36	590	53	1343	33	1257	38	1497	35	1610	29	0	.	237002	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	.	12	56	10	61	33	43	332	18	180	18	167	33	41	39	0	100	8	100	8	71	0	.	4	73	44	43	48	37	73	85	3	71	13	43	36	46	32	48	75	43	14	40	22	67	57	33	79	38	133	30	124	25	20	76	44	40	2	100	9	85	15	52	16	41	21	.	8	52	15	54	32	69	48	58	6	71	42	35	20	80	6	61	0	.	6	73	12	60	42	73	12	55	12	206	52	49	126	52	42	43	17	71	0	.	2	100	33	80	78	40	173	31	3	100	46	69	78	39	20	45	6	85	18	84	6	71	41	55	25	52	19	66	13	88	10	52	0	.	0	.	14	54	12	90	13	86	11	70	7	72	2	100	0	.	30	81	118	42	95	31	29	38	8	85	37	63	23	42	40	32	22	39	6	83	35	90	10	85	0	.	3256	6
	16-23 cm	34	53	13	79	0	.	125	88	60	49	149	36	767	16	717	21	691	22	171	39	9	71	33	77	28	71	0	.	48	77	186	34	192	41	242	65	24	71	65	65	66	35	165	44	247	42	82	41	105	53	174	32	250	36	302	32	377	22	31	76	140	46	5	100	14	100	140	47	128	53	24	.	50	68	90	50	144	62	114	49	75	91	115	38	59	50	24	92	7	.	25	74	64	52	79	49	31	44	49	50	120	37	78	58	309	41	33	74	0	.	21	117	97	76	303	38	629	26	3	100	96	79	264	41	88	45	55	64	37	71	64	56	48	55	137	52	84	59	55	86	56	64	0	.	0	.	47	53	41	82	20	100	57	59	79	69	57	95	0	.	19	77	307	35	329	29	193	37	22	85	38	59	49	58	205	39	73	49	53	95	23	72	21	100	0	.	10940	5
	24-35 cm	113	51	69	65	0	.	276	65	304	51	546	35	2466	14	1816	18	1501	20	684	40	14	100	51	73	121	84	0	.	73	73	525	45	382	42	344	35	43	72	272	43	188	50	334	47	376	35	354	49	179	47	587	31	491	30	595	31	1194	23	181	83	445	48	15	100	84	83	293	41	365	50	85	.	175	48	391	37	340	52	226	56	265	49	676	29	278	55	66	100	13	.	70	82	330	54	143	47	163	54	165	53	823	34	363	46	362	43	23	100	0	.	85	72	73	71	568	30	789	24	0	.	226	68	325	39	345	52	228	58	37	73	168	52	178	71	437	57	231	57	29	100	200	49	16	.	0	.	102	68	171	61	62	69	208	54	144	53	99	82	0	.	123	63	583	32	572	29	330	32	56	62	127	73	127	48	670	31	608	30	157	63	49	85	44	71	0	.	28405	4
	36-51 cm	691	34	155	46	0	.	474	48	680	44	1408	30	5522	13	3528	15	3438	16	1061	32	17	158	157	100	234	50	0	.	120	81	919	36	844	34	866	30	209	72	504	40	1207	30	310	43	835	32	444	43	193	52	1549	24	1434	39	1561	24	2249	22	165	55	663	35	148	76	134	82	858	33	330	42	22	.	452	57	781	36	754	42	203	42	484	54	1593	25	243	54	156	75	90	.	146	72	389	44	333	54	397	46	338	58	703	31	484	45	510	35	31	71	0	.	127	75	17	100	476	30	688	29	21	100	84	58	394	40	324	44	389	68	163	73	330	51	173	59	510	43	265	56	206	59	236	51	129	.	0	.	288	51	95	69	213	83	279	55	192	59	181	81	0	.	225	58	960	33	525	31	621	36	198	64	357	59	417	42	1135	26	1347	29	335	46	269	73	72	83	0	.	53252	4
	=52 cm	526	37	199	49	0	.	847	43	640	50	1254	36	5950	15	5608	15	4244	18	587	38	169	88	0	.	294	52	0	.	47	100	631	47	940	37	757	30	87	100	352	53	1410	28	238	50	888	35	449	41	276	67	1182	29	1245	37	1854	26	2501	27	278	62	434	44	149	100	63	89	743	34	441	46	31	.	576	56	757	56	345	52	672	45	551	47	2281	30	130	77	400	63	0	.	152	79	58	58	65	72	462	48	287	64	636	41	824	53	588	45	80	73	0	.	38	74	19	100	367	38	425	36	221	82	67	100	444	47	363	48	119	71	87	71	305	58	193	55	527	49	376	58	42	71	71	58	44	.	0	.	35	72	97	100	47	100	230	56	49	71	217	78	0	.	279	64	1166	35	679	36	396	47	210	65	319	72	250	49	1179	33	508	44	477	58	85	72	202	71	0	.	54310	4
	total	1354	33	451	43	0	.	1734	40	1693	42	3390	28	15037	12	11849	13	10041	14	2545	28	209	79	248	87	685	51	0	.	293	76	2304	32	2405	29	2281	28	367	75	1206	39	2907	26	1078	40	2420	29	1343	36	776	46	3549	22	3499	27	4445	21	6444	21	676	51	1725	33	319	74	304	70	2048	31	1280	38	182	.	1261	51	2033	34	1615	37	1264	38	1381	44	4708	23	730	48	652	58	110	.	398	70	854	44	661	47	1064	44	850	47	2334	29	1876	38	1811	29	184	71	0	.	273	71	238	73	1793	27	2705	20	248	76	519	58	1504	33	1140	40	797	59	341	71	872	46	633	45	1635	41	976	52	345	60	573	46	190	.	0	.	486	49	414	60	355	70	784	51	472	52	556	72	0	.	676	58	3134	27	2199	25	1569	32	494	61	878	54	866	37	3229	24	2557	27	1027	46	461	64	350	71	0	.	150163	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	56	28	-6	333	49	44	478	18	217	23	400	18	257	19	26	53	55	44	70	26	0	.	67	80	106	37	106	28	97	65	6	61	78	23	158	27	105	23	205	25	454	24	329	23	240	17	126	30	161	25	165	21	73	40	70	31	19	87	34	46	74	40	44	38	89	35	90	36	43	35	60	43	113	30	78	44	67	28	130	29	90	31	68	42	76	67	28	62	93	39	69	58	29	86	82	36	174	41	55	35	87	41	87	45	47	31	139	31	92	36	234	25	172	27	133	32	173	25	65	32	36	62	58	37	83	38	77	36	65	40	34	44	15	78	70	38	24	61	51	55	76	30	45	39	64	40	42	35	41	53	24	65	0	.	34	74	318	21	319	23	306	25	33	43	63	43	0	14262	56	28	57	29	32	33	42	76	62	43	0	.	9391	4
	16-23 cm	267	29	195	37	202	44	293	42	81	37	298	29	1087	13	955	17	1309	15	910	18	73	45	102	40	218	33	0	.	113	41	409	28	507	25	431	42	43	52	324	28	378	28	398	23	481	26	1085	14	1067	15	879	16	374	26	414	26	529	19	129	41	238	32	79	38	110	36	224	35	300	37	244	30	323	28	198	36	285	37	389	26	363	37	192	31	464	26	183	34	189	40	105	45	184	29	270	32	218	37	157	42	279	29	198	37	411	34	194	46	387	27	174	31	449	29	459	32	771	22	564	24	475	28	480	28	263	33	111	50	257	31	170	35	125	34	207	40	165	49	102	54	223	28	53	53	56	44	270	28	221	40	243	35	116	37	237	46	123	57	8	100	30	61	1029	17	978	20	878	18	128	41	142	41	142	51	252	34	206	47	243	42	109	36	243	41	0	.	30439	3
	24-35 cm	514	26	540	35	442	35	612	34	542	34	1028	27	3568	12	2598	14	2658	14	2884	17	161	51	344	36	606	30	2	.	471	45	1143	29	1067	25	822	29	193	57	976	32	1315	20	979	22	962	22	2968	14	2826	13	2008	15	791	23	1211	27	1591	20	322	52	641	35	227	38	276	39	493	29	933	32	527	34	1096	25	523	31	645	34	853	28	618	32	1035	24	1282	22	490	32	523	33	312	37	716	33	456	27	720	31	484	31	1099	28	566	33	647	31	84	43	672	27	594	30	491	28	801	27	1063	22	766	24	858	26	803	25	755	35	288	47	607	34	488	33	425	36	634	43	446	49	155	65	994	27	109	64	133	50	718	29	492	30	516	34	451	36	349	34	189	50	192	62	139	57	2016	15	1956	17	2020	16	270	36	331	38	295	35	882	27	880	25	375	36	404	40	368	32	0	.	75312	2
	36-51 cm	1358	22	537	26	668	34	1655	22	1347	30	2308	24	7720	11	5093	12	5569	12	4313	14	277	55	700	38	980	27	106	.	688	41	1770	24	1739	24	1786	23	285	56	2142	22	3310	16	1573	19	2472	20	4707	13	5111	12	5331	13	1937	30	2286	19	2731	19	465	34	998	27	592	35	541	36	1415	25	834	31	491	31	1522	25	1273	28	1269	30	1342	22	1126	29	1932	21	2023	21	762	27	1201	28	850	30	1139	25	1180	27	1022	27	1233	27	881	29	798	33	1025	25	330	44	870	22	1200	27	273	32	597	25	899	25	800	23	564	29	900	26	674	32	522	52	986	32	942	34	741	36	777	32	473	38	364	45	1100	24	290	56	327	44	1015	24	568	33	1327	26	981	26	699	45	535	43	251	55	240	55	3015	16	2485	16	3306	16	462	38	679	36	851	29	1257	24	1753	24	653	32	886	37	514	33	0	.	131513	2
	=52 cm	1271	26	879	32	680	37	3084	22	1193	36	1959	28	8155	13	8069	13	7984	13	2470	17	507	43	414	64	869	32	70	.	322	50	1383	31	1945	27	1535	25	87	100	2639	23	3396	20	2201	21	3361	19	5361	14	4170	16	3827	15	2063	28	2687	23	2877	24	668	42	964	37	588	47	552	42	1448	25	645	39	485	46	1589	28	1349	37	588	37	1796	28	2257	28	3461	25	1269	26	1588	32	388	45	823	37	400	38	1810	29	1056	30	1331	34	715	37	1282	40	1311	31	862	37	1536	30	946	34	378	33	452	32	526	33	1052	28	454	34	1079	33	722	37	517	45	651	46	1040	33	1145	33	1039	37	657	42	42	71	1083	35	247	62	377	58	670	31	812	38	545	36	1142	34	252	44	526	42	433	68	657	49	4125	17	2225	19	3502	18	404	44	1319	36	627	34	1372	31	1005	32	981	44	517	39	773	37	0	.	140509	2
	total	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	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. 2293029. Birmensdorf, Swiss Federal Research Institute WSL

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