Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI3
number of stems
conifers and broadleaves · diameter at breast height (main diameter classes)
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
conifers and broadleaves	diameter at breast height (main diameter classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
conifers	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	365	40	313	42	301	63	235	44	160	61	427	56	2539	14	1924	16	2039	22	1519	21	18	100	139	69	208	39	0	.	48	58	652	33	671	36	420	56	0	.	500	30	1210	27	1040	28	991	33	3164	17	3029	16	1261	21	1083	34	810	24	1401	24	143	57	624	39	132	48	220	55	560	31	359	59	690	35	688	47	240	56	377	59	570	28	116	74	437	37	657	46	431	43	158	89	52	58	264	46	369	44	669	50	100	55	604	43	427	35	618	48	333	40	548	43	398	33	35	100	0	.	0	.	566	31	157	48	90	82	463	38	91	60	619	37	321	41	248	48	446	50	178	45	85	72	606	36	267	77	87	59	544	30	382	64	312	48	283	45	241	65	120	63	0	.	0	.	2770	17	2156	21	1301	19	178	38	432	58	138	58	277	55	272	50	52	74	33	71	272	52	0	.	51277	4
	16-23 cm	382	49	592	47	406	58	290	56	298	41	610	41	3381	13	2129	20	2469	18	1755	28	17	100	138	53	156	51	0	.	184	56	864	34	774	32	485	38	17	100	904	31	1781	23	1469	25	1323	25	3421	16	4776	14	1731	22	710	30	1180	27	1659	26	257	53	393	27	320	47	217	41	742	33	804	54	1013	32	787	36	362	40	714	44	693	36	229	37	390	36	1126	46	417	40	297	48	187	59	317	39	429	32	967	34	338	35	614	37	498	41	1237	41	248	58	908	29	639	33	0	.	0	.	0	.	564	37	140	47	306	58	472	39	293	49	938	33	398	39	198	40	349	45	222	56	136	63	420	40	158	52	104	57	720	29	432	56	650	59	238	60	366	68	137	49	31	100	34	100	4022	16	3349	19	2565	20	429	46	312	68	174	81	993	45	375	60	294	68	53	58	273	42	0	.	69193	3
	24-35 cm	383	41	210	65	221	52	118	43	660	36	702	35	4195	12	2388	16	2021	20	2072	20	0	0	146	54	191	41	52	.	401	48	1093	35	517	34	537	39	138	88	1029	47	1619	19	1427	23	1281	22	3825	15	4254	13	1229	23	652	27	1174	25	1765	24	261	63	729	41	195	48	222	50	495	30	604	40	835	34	1009	35	327	36	358	52	926	29	301	40	722	25	660	36	380	40	326	45	103	58	179	34	329	32	814	29	307	36	649	50	490	39	448	67	71	61	959	30	657	34	136	63	0	.	0	0	626	33	271	49	269	51	452	37	385	52	715	35	305	47	302	36	629	59	267	56	103	62	592	34	198	52	141	53	847	28	141	43	271	38	307	53	84	100	288	59	118	59	68	71	2951	15	3147	17	2377	18	212	61	186	51	202	48	709	37	539	32	154	51	199	61	204	45	0	.	66047	3
	36-51 cm	185	35	137	56	127	49	512	28	626	28	904	25	3526	11	1998	13	1782	15	1633	17	22	75	42	62	307	42	49	.	220	45	625	31	635	27	621	28	7	100	688	24	1697	19	886	19	1281	21	2604	13	3313	12	1338	18	437	32	1082	19	1221	20	128	48	424	27	253	39	149	44	561	25	233	33	300	33	580	29	521	30	224	37	475	26	304	38	506	26	437	28	185	34	226	37	124	50	214	31	458	30	616	25	346	33	250	34	215	36	226	31	204	47	616	27	624	29	56	64	0	.	23	76	337	31	163	38	173	42	219	43	271	54	463	32	281	44	412	33	340	35	134	47	58	60	325	28	138	67	204	48	545	24	113	44	296	31	417	30	137	81	180	52	91	63	62	63	2008	15	1614	15	1841	16	140	41	128	38	270	32	406	29	580	26	187	36	199	57	163	38	0	.	50879	2
	=52 cm	86	39	58	40	73	52	452	26	282	32	349	27	1694	13	1367	14	1244	14	562	18	8	100	35	82	97	45	7	.	79	59	217	34	322	29	274	29	0	0	448	27	695	20	447	21	811	18	1276	14	1161	14	572	19	299	35	572	23	616	23	102	40	204	31	128	46	130	44	344	27	117	45	132	44	296	31	165	41	104	47	275	30	379	31	610	27	174	30	190	42	59	51	42	57	31	81	347	31	297	31	250	42	53	43	134	43	224	32	184	37	338	28	282	31	28	61	15	71	22	73	157	32	88	44	107	57	127	45	115	44	151	39	228	35	189	36	191	34	160	50	7	100	246	35	13	100	90	57	144	28	151	46	114	39	195	41	38	84	112	60	53	66	78	79	1096	17	649	18	890	18	64	53	178	40	127	35	171	34	167	31	158	45	86	48	95	44	0	.	26193	3
	total	1399	31	1310	39	1128	45	1607	24	2026	30	2992	25	15334	10	9806	12	9556	13	7540	16	66	53	501	44	959	32	108	.	932	40	3450	27	2919	23	2336	26	162	86	3569	27	7002	16	5268	19	5687	17	14290	12	16534	11	6132	15	3182	24	4818	18	6661	17	892	43	2374	26	1028	33	938	36	2702	24	2118	43	2970	30	3361	28	1614	28	1777	39	2939	24	1329	30	2664	21	3053	31	1603	30	1065	41	507	39	1005	32	1933	24	3363	24	1342	29	2171	34	1765	31	2754	35	1040	36	3368	24	2600	25	255	58	15	71	45	53	2251	26	819	34	946	42	1733	33	1156	44	2886	29	1533	30	1349	29	1955	41	962	39	389	52	2189	28	774	43	626	40	2800	23	1218	44	1644	40	1439	31	867	63	837	47	293	57	242	53	12848	12	10915	15	8975	15	1024	37	1237	42	912	35	2556	32	1933	30	845	40	570	48	1006	32	0	.	263589	2
broadleaves	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	479	32	258	35	424	54	894	48	94	69	189	48	1616	19	1070	18	1355	16	1899	20	199	48	276	37	518	28	0	.	155	43	389	43	522	34	302	38	104	57	990	35	286	33	244	33	960	42	1685	30	330	54	1709	22	328	36	561	39	878	28	325	54	498	34	155	55	227	46	217	48	274	43	146	57	719	47	308	32	436	44	896	33	532	40	537	32	693	37	468	34	315	43	307	35	272	44	574	38	229	42	216	63	689	31	169	40	372	37	779	60	582	41	252	45	1722	29	1054	35	2704	22	1561	29	1117	28	1492	29	383	36	202	83	143	44	384	44	482	39	285	51	294	45	139	66	240	33	36	71	348	68	226	48	295	51	270	33	279	43	420	51	103	62	19	100	56	73	1112	41	729	30	1893	21	121	87	139	47	286	34	446	29	134	43	310	50	68	61	358	39	0	.	49751	4
	16-23 cm	886	30	444	31	492	36	700	32	87	61	312	39	1978	20	1712	18	2503	16	2316	19	395	56	266	40	639	39	0	.	324	37	623	28	1020	34	493	36	261	51	1364	39	275	37	635	33	943	35	1522	24	754	48	1881	21	560	27	549	29	989	26	205	53	427	35	107	46	171	48	245	39	688	47	61	79	718	34	436	38	650	33	886	30	844	41	781	32	827	27	313	42	700	31	546	40	546	34	480	42	187	53	457	45	653	29	313	43	417	36	419	43	661	41	298	44	2060	29	2009	26	3750	20	1971	26	1667	29	2145	26	591	38	129	87	157	60	567	42	447	40	437	39	263	45	258	65	480	36	36	71	127	56	328	42	467	49	456	35	309	39	623	48	187	47	39	100	132	63	677	29	951	31	1931	20	164	53	312	38	256	50	359	38	299	30	602	45	384	37	428	45	0	.	63956	3
	24-35 cm	602	31	277	38	288	38	759	30	69	62	616	32	1521	23	1596	19	2076	16	2306	18	240	49	395	43	797	29	0	.	131	60	406	37	1020	34	464	38	288	53	873	34	480	41	272	37	345	50	1322	29	282	54	1642	20	372	33	549	30	738	28	131	52	265	34	149	51	180	74	194	37	725	45	37	72	588	34	554	33	801	35	627	30	630	44	1209	25	1044	28	433	45	559	38	393	37	977	31	378	32	154	49	266	38	533	38	364	33	470	31	53	74	402	34	208	56	692	32	1428	24	2023	20	948	28	981	29	1245	26	564	40	18	100	114	71	421	42	304	54	371	42	369	46	53	58	752	31	17	100	95	67	140	55	573	35	488	35	273	55	392	39	137	55	108	63	56	73	553	45	125	51	975	25	130	48	294	41	271	39	616	29	477	32	204	49	288	51	186	39	0	.	49732	3
	36-51 cm	677	32	360	27	140	53	464	26	116	48	187	39	705	21	701	18	1288	15	885	18	159	47	348	39	291	29	0	.	109	64	318	34	282	34	300	33	96	67	623	35	233	41	185	32	85	46	269	28	69	57	1449	16	607	46	191	37	330	36	178	44	100	44	100	43	76	47	146	39	151	44	41	69	293	35	309	32	517	38	341	27	324	32	630	33	554	22	287	34	330	32	310	35	549	26	241	32	60	89	269	33	238	32	192	35	274	29	7	100	133	33	56	77	119	33	465	26	618	27	315	35	270	28	438	28	161	40	7	100	34	63	260	40	74	44	102	55	105	49	38	71	328	30	83	64	65	47	68	42	255	35	333	31	217	43	264	38	109	54	82	52	75	47	87	48	46	86	249	30	142	43	223	37	164	32	303	28	272	27	171	36	237	42	164	34	0	.	25119	3
	=52 cm	265	27	137	33	58	43	192	35	20	58	64	49	270	31	288	28	460	20	109	32	125	38	35	52	90	41	0	.	7	100	82	50	30	61	107	37	23	100	163	39	63	57	75	37	15	70	92	40	20	73	313	22	144	38	85	49	56	57	11	100	7	100	14	70	0	.	8	100	14	100	0	.	103	43	75	55	40	45	86	47	63	48	142	32	110	40	192	36	42	57	99	40	124	47	38	61	35	100	75	49	95	51	125	47	125	39	0	.	84	49	16	100	106	39	110	34	104	41	125	35	31	61	177	39	48	63	0	.	15	71	76	46	69	66	19	75	72	53	0	.	51	55	38	74	0	.	8	100	56	58	52	60	45	47	14	71	42	85	16	70	63	58	15	71	0	.	94	39	27	78	90	41	49	48	122	39	106	41	88	56	46	51	74	47	0	.	7158	5
	total	2910	25	1477	25	1401	32	3008	26	385	44	1368	29	6090	16	5366	15	7682	12	7514	15	1117	42	1320	33	2337	27	0	.	726	37	1818	29	2874	28	1666	27	771	46	4014	32	1338	28	1411	28	2348	36	4890	23	1454	44	6994	15	2011	24	1935	27	2991	22	850	39	1297	29	525	34	655	41	810	30	1852	39	285	45	2421	32	1681	28	2443	31	2836	25	2395	34	3300	26	3228	22	1692	28	1947	27	1655	30	2468	26	1712	32	666	35	1282	35	2209	26	1164	29	1659	25	1258	51	1863	31	830	44	4699	25	5065	22	9198	17	4919	24	4067	25	5498	22	1747	34	357	85	462	49	1708	36	1375	36	1214	37	1104	39	487	57	1851	26	210	61	635	48	771	32	1646	36	1599	28	1123	32	1711	37	578	44	264	60	382	60	2444	29	1851	27	5142	18	584	44	1057	33	1026	33	1846	24	1288	25	1374	34	1024	34	1210	32	0	.	195715	2
indeterminable	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	.	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	.	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	.	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	.	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	.	0	.	0	0
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	843	28	571	31	724	41	1129	46	254	49	616	43	4155	13	2994	13	3394	16	3418	17	217	50	415	41	726	25	0	.	203	35	1041	28	1193	27	722	39	104	57	1490	28	1497	25	1284	24	1950	27	4848	16	3359	16	2971	17	1412	29	1372	24	2280	20	468	43	1122	31	286	37	447	37	777	29	633	40	836	33	1407	35	548	37	813	45	1466	27	649	38	973	27	1350	30	899	32	473	44	358	35	536	36	944	34	898	39	316	47	1293	32	597	29	990	34	1113	45	1129	32	650	28	1757	28	1054	35	2704	22	2127	24	1275	27	1582	28	847	33	294	71	761	34	705	32	729	35	731	39	472	38	224	65	846	31	303	69	435	62	770	27	677	43	583	34	562	38	661	46	222	60	19	100	56	73	3882	17	2886	18	3194	16	299	42	571	45	424	33	723	33	406	37	362	45	101	47	630	33	0	.	101028	3
	16-23 cm	1268	27	1036	35	898	34	991	30	384	36	922	31	5359	12	3841	15	4972	13	4071	17	412	54	405	34	795	34	0	.	509	41	1487	24	1794	26	979	29	278	50	2269	27	2056	20	2103	22	2266	22	4943	14	5530	14	3612	16	1270	23	1730	24	2647	20	462	39	820	26	427	41	388	40	987	28	1493	36	1074	31	1505	27	797	33	1364	32	1579	28	1073	36	1171	28	1953	29	730	33	997	30	733	36	862	28	909	31	1154	32	795	34	1268	26	811	34	1654	32	667	37	1569	25	937	27	2060	29	2009	26	3750	20	2535	22	1807	28	2451	24	1063	31	421	43	1095	31	965	33	645	33	786	39	485	42	393	55	900	30	195	52	232	40	1048	28	899	37	1106	38	546	42	989	41	325	41	70	71	166	65	4699	15	4300	17	4496	15	593	37	624	39	430	50	1352	37	675	36	896	38	437	37	701	33	0	.	133149	2
	24-35 cm	984	30	487	37	509	35	877	26	729	35	1318	27	5716	11	3983	14	4097	13	4378	15	240	49	541	38	989	27	52	.	532	41	1499	28	1538	26	1000	28	426	49	1903	31	2099	18	1699	21	1626	21	5147	13	4536	12	2871	15	1024	22	1722	22	2503	20	393	47	994	32	344	38	402	48	689	27	1329	32	872	33	1597	26	881	26	1158	30	1553	24	931	33	1932	22	1704	23	813	32	885	31	496	33	1156	28	707	24	968	27	573	30	1182	34	854	30	918	36	124	55	1362	25	864	29	827	29	1428	24	2023	20	1574	22	1253	25	1514	23	1016	34	404	50	828	33	726	31	606	36	1000	43	637	40	156	51	1344	26	214	53	236	48	988	26	714	30	759	29	579	38	475	36	425	50	226	56	123	51	3504	14	3272	17	3353	15	342	43	481	37	474	37	1326	26	1015	24	358	38	487	40	390	37	0	.	115779	2
	36-51 cm	862	27	497	27	267	39	976	21	742	28	1092	23	4231	10	2699	12	3071	11	2517	14	181	45	389	38	598	29	49	.	329	38	944	24	917	23	921	23	103	64	1311	22	1930	17	1071	18	1367	20	2873	12	3382	11	2787	13	1044	31	1273	19	1551	18	307	34	524	26	353	34	226	39	707	23	384	29	341	30	873	24	830	25	741	29	816	21	628	29	1136	23	991	21	471	28	556	27	434	31	763	25	700	25	676	24	616	25	489	27	407	30	500	25	211	46	749	23	680	27	175	32	465	26	641	26	652	23	433	27	612	24	379	33	279	53	497	31	541	31	486	29	442	31	240	41	96	52	652	23	221	54	269	44	614	22	367	31	629	25	634	28	401	41	289	42	173	55	137	46	2095	15	1659	15	2090	14	282	38	351	31	434	29	709	25	852	22	358	30	436	38	327	30	0	.	75998	2
	=52 cm	351	26	195	30	131	36	643	22	302	31	413	27	1964	12	1655	13	1705	12	672	17	133	36	70	58	187	32	7	.	86	55	298	29	352	27	380	24	23	100	611	23	758	19	522	20	826	17	1369	13	1181	14	885	15	443	28	657	22	672	22	113	38	211	31	142	43	130	44	351	26	131	41	132	44	400	27	240	34	144	38	362	26	442	29	752	23	284	25	382	31	101	42	140	36	155	52	385	29	332	29	325	36	148	37	260	37	349	27	184	37	422	26	298	30	134	35	125	31	126	40	282	24	119	36	284	32	175	37	115	44	166	38	305	31	258	33	210	34	233	41	7	100	297	30	51	61	90	57	151	28	207	37	166	36	240	36	52	65	154	49	69	66	142	52	1112	17	649	18	983	16	91	47	268	34	176	32	293	28	273	28	246	37	133	39	169	35	0	.	33351	2
	total	4309	23	2786	25	2529	29	4615	22	2411	29	4360	23	21424	9	15172	11	17238	10	15054	13	1183	41	1820	30	3295	24	108	.	1658	33	5268	22	5794	20	4002	21	933	46	7583	22	8340	15	6680	17	8034	16	19180	11	17988	11	13126	12	5192	19	6753	18	9652	16	1742	31	3671	23	1553	32	1593	33	3512	22	3970	30	3255	28	5782	22	3295	24	4220	27	5775	21	3723	27	5964	21	6281	20	3294	24	3012	26	2161	27	3473	25	3644	23	4029	23	2624	26	4379	24	2929	25	4412	25	2298	34	5231	20	3429	22	4954	24	5080	22	9244	17	7170	19	4886	23	6444	21	3480	29	1513	41	3348	27	3242	25	2724	27	3169	34	2066	32	876	51	4039	23	984	43	1261	36	3571	22	2864	29	3243	27	2562	27	2578	36	1414	39	557	54	624	48	15292	12	12766	14	14116	12	1608	31	2294	30	1938	29	4402	24	3221	22	2219	28	1593	33	2216	26	0	.	459304	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. 2299166. Birmensdorf, Swiss Federal Research Institute WSL

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