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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NFI2
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 1993/95
		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	550	46	507	61	217	72	119	52	219	48	406	59	3068	16	2166	16	2462	22	1592	25	0	0	242	67	243	43	0	.	345	54	742	29	703	31	425	40	17	100	646	33	1326	29	1112	24	755	29	2232	18	2936	17	1584	21	953	34	1092	33	1401	21	224	64	384	33	91	52	170	50	610	34	402	70	817	32	415	48	382	51	492	47	580	29	194	51	632	41	1005	40	308	52	295	62	223	67	249	46	334	43	856	41	107	50	608	45	545	43	1155	46	161	60	604	34	379	34	18	100	0	.	0	.	325	42	127	51	146	63	492	39	54	75	790	40	229	50	157	45	421	55	334	75	172	82	612	33	89	61	70	50	574	29	374	69	499	53	160	46	250	88	289	48	0	.	34	71	3281	17	2389	22	1579	20	623	48	190	73	269	54	1033	43	666	62	241	80	33	71	243	51	0	.	57044	4
	16-23 cm	391	36	636	60	318	52	250	42	409	45	919	35	4301	14	2742	17	2745	21	1708	22	17	100	364	86	157	46	35	.	610	50	1373	30	1170	30	1057	41	192	82	820	32	1528	21	1416	26	1143	22	3542	17	4584	14	2158	26	725	33	1603	28	2208	22	376	79	727	45	337	48	234	47	725	37	753	51	851	39	789	35	412	47	530	48	949	35	309	33	601	30	538	43	379	43	224	57	325	58	214	38	377	35	1252	34	359	35	605	44	688	43	879	48	107	70	902	27	560	32	83	81	0	.	0	.	605	34	174	49	143	50	411	41	382	46	892	34	361	43	105	53	608	47	249	46	172	61	711	39	332	47	106	53	725	32	246	43	502	58	384	46	102	62	475	55	74	100	71	61	4104	16	3500	18	2381	21	511	51	121	55	275	48	761	42	490	46	311	52	33	71	412	41	0	.	74934	3
	24-35 cm	364	45	189	58	302	44	482	34	768	31	1040	36	4879	13	3115	15	2619	17	2115	19	19	100	99	46	314	36	122	.	262	57	1054	34	1370	26	925	29	34	100	1104	50	1947	18	1239	25	1311	23	3814	16	4249	13	1894	21	771	30	1202	21	1745	23	263	48	544	39	266	40	249	37	798	31	609	36	702	34	934	32	535	32	246	48	818	28	215	47	839	32	607	31	108	60	433	36	157	46	214	33	418	32	1103	30	402	36	502	46	388	38	190	67	123	55	681	36	713	36	53	74	0	.	0	.	510	35	219	58	234	48	361	33	385	55	947	33	98	52	382	39	489	45	123	52	138	88	446	29	370	54	141	61	871	27	75	62	355	34	381	43	33	100	224	60	105	56	358	61	3239	15	2595	17	2522	18	206	51	135	43	473	38	469	32	691	34	240	41	315	60	138	46	0	.	70728	3
	36-51 cm	260	39	394	41	119	50	1033	22	547	29	918	25	4301	12	2821	12	2331	13	1618	16	61	79	35	67	299	42	14	.	256	46	806	27	952	21	1067	26	7	100	722	24	1289	18	817	20	1494	19	2700	13	3054	12	1435	18	571	26	1506	20	1441	19	198	45	414	26	277	36	165	42	563	24	226	37	433	33	741	28	446	32	246	46	465	27	366	38	727	27	527	25	321	35	275	39	364	41	241	28	306	30	856	25	331	30	393	38	249	34	276	29	240	38	543	27	602	28	50	71	0	.	38	53	222	36	171	43	160	52	192	49	249	50	401	33	310	43	345	34	321	33	251	43	125	84	310	29	92	56	181	40	413	25	172	41	281	42	491	29	103	61	139	44	66	63	366	52	1910	15	1552	16	1739	15	212	48	180	42	575	32	661	26	631	25	316	33	238	41	241	40	0	.	57361	2
	=52 cm	94	40	209	39	74	47	309	26	158	43	199	31	1456	13	1479	13	1246	14	383	21	61	100	28	100	63	48	0	.	85	50	181	38	432	27	184	30	7	100	375	30	564	21	468	22	643	19	1112	14	944	15	468	20	313	34	481	22	517	24	80	47	134	34	100	41	95	60	290	30	90	49	126	35	217	33	183	40	147	54	264	31	270	33	488	29	178	28	150	41	50	47	101	57	33	60	276	31	218	29	209	36	86	44	114	41	207	31	180	35	249	32	173	39	28	61	15	71	7	100	127	35	65	43	100	58	125	46	107	52	131	42	162	36	109	40	128	40	107	43	0	.	187	39	21	58	106	50	115	29	114	48	64	43	161	39	30	81	97	61	38	67	82	62	866	17	480	20	725	19	65	48	87	49	122	38	176	30	114	33	116	43	100	59	97	43	0	.	22415	3
	total	1659	30	1934	41	1030	44	2193	22	2102	30	3481	26	18006	10	12322	11	11403	13	7415	15	158	64	768	65	1075	33	171	.	1558	40	4157	24	4628	22	3658	25	258	81	3667	28	6654	16	5052	19	5346	17	13398	12	15768	11	7538	17	3331	24	5884	18	7312	18	1142	48	2202	29	1071	34	912	33	2987	25	2081	41	2929	30	3096	28	1958	30	1661	37	3075	23	1355	31	3287	26	2856	25	1267	31	1277	38	1169	38	950	28	1712	24	4285	26	1408	28	2195	34	1984	33	2706	36	809	36	2979	24	2425	26	231	56	15	71	45	53	1789	28	756	34	783	40	1582	32	1178	45	3161	29	1160	32	1099	31	1966	41	1064	41	608	59	2266	27	904	42	604	37	2698	23	982	40	1700	38	1577	30	519	62	1223	46	283	57	911	48	13399	13	10516	15	8945	16	1618	41	713	38	1714	29	3100	28	2592	29	1225	37	719	45	1131	33	0	.	282482	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	685	29	433	31	295	37	449	29	94	83	125	43	1645	22	1081	20	1924	19	1695	21	251	58	227	45	518	34	0	.	212	41	350	35	762	30	496	30	290	52	853	33	286	36	361	38	986	34	1957	29	441	58	1613	23	282	35	643	35	1028	23	245	57	422	35	121	61	252	67	199	44	552	64	182	64	668	40	267	43	700	39	690	34	514	38	780	40	702	27	362	40	446	36	548	35	399	35	465	43	252	40	274	57	365	37	259	45	459	42	560	56	470	34	264	47	1965	30	1420	28	3501	22	1542	27	1072	30	1576	28	402	34	202	83	245	48	382	54	358	44	307	41	139	55	207	72	324	43	17	100	176	60	374	34	418	41	265	40	208	47	538	54	241	50	39	100	333	69	858	27	687	30	1895	21	128	64	208	46	218	60	364	33	244	47	487	36	127	38	205	42	0	.	53072	4
	16-23 cm	692	26	633	32	343	37	903	32	120	57	406	35	1767	22	1788	18	2540	15	2939	17	289	56	348	36	849	39	0	.	291	42	657	31	1282	31	422	35	619	60	1112	32	610	38	709	32	478	44	1753	26	596	42	1883	20	517	28	506	27	969	28	190	49	420	35	215	46	240	42	219	41	568	41	104	62	768	35	402	35	577	37	813	32	810	48	961	29	1100	27	260	39	585	34	631	37	649	32	595	45	195	56	431	55	443	35	415	37	403	31	329	49	493	34	195	61	1661	32	2249	25	3348	20	1617	28	1732	31	2261	24	644	41	110	100	169	58	547	39	502	46	448	41	249	51	138	77	659	35	36	71	166	46	259	39	727	46	733	39	309	43	545	46	223	44	63	72	223	54	703	32	528	36	1912	22	147	50	258	44	220	51	608	36	466	35	295	46	429	37	188	51	0	.	64402	3
	24-35 cm	814	25	553	31	240	46	623	29	99	53	609	31	1565	23	1484	19	2247	16	1916	18	251	46	592	34	711	31	0	.	189	63	540	39	903	42	600	35	229	46	681	32	330	44	241	42	314	58	769	32	191	53	1885	18	725	37	405	42	724	29	148	47	248	40	281	47	207	55	204	35	512	37	22	100	516	35	574	34	879	38	682	31	718	35	1233	26	971	26	415	47	718	31	376	39	1082	29	328	36	115	54	434	35	551	32	254	42	582	28	35	70	253	42	133	52	417	32	1295	24	1519	21	570	32	704	32	1340	27	359	43	18	100	49	73	381	43	302	54	345	47	313	48	34	100	713	33	75	62	202	50	121	61	584	31	626	34	395	54	400	40	189	46	58	74	82	66	279	45	96	53	855	27	130	48	281	51	168	40	761	30	520	29	273	44	305	42	117	55	0	.	47775	3
	36-51 cm	632	22	373	31	130	47	524	28	95	53	185	37	671	23	696	19	1519	14	730	19	198	42	288	46	215	30	0	.	122	59	143	37	206	41	280	38	57	89	472	25	209	41	179	32	73	42	162	32	51	75	1351	15	507	42	184	36	307	32	153	46	54	48	70	52	70	44	111	39	110	45	41	69	314	40	249	34	399	39	278	29	208	32	546	37	459	25	310	32	305	31	314	35	472	27	182	36	60	89	199	36	206	35	202	34	231	27	7	100	80	36	63	89	110	39	317	26	354	31	256	33	166	35	343	29	118	45	7	100	34	63	156	44	146	50	84	52	108	48	23	100	295	32	132	54	72	50	49	51	162	38	287	29	203	43	196	46	104	45	65	53	68	61	48	57	19	100	182	35	141	44	180	34	170	39	335	29	213	29	201	32	220	42	202	38	0	.	22015	3
	=52 cm	222	32	123	39	53	42	133	39	14	71	46	49	222	32	224	31	381	20	77	35	133	45	28	61	83	44	0	.	14	100	48	43	23	58	75	52	29	100	90	41	69	61	33	52	6	100	53	51	7	100	284	21	103	40	49	46	18	100	30	76	7	100	7	100	7	100	8	100	21	74	0	.	124	53	58	53	45	52	70	49	52	54	108	40	96	40	110	42	35	53	29	63	67	40	26	60	36	100	58	55	86	45	74	52	92	38	0	.	77	81	32	100	58	49	87	40	82	53	136	34	31	78	194	32	36	74	0	.	15	71	69	54	35	62	0	.	55	63	0	.	29	61	25	100	8	100	0	.	43	72	29	61	21	73	7	100	41	85	8	100	56	56	0	.	0	.	45	46	14	71	56	61	48	47	112	39	149	47	55	43	54	53	33	53	0	.	5619	5
	total	3045	19	2115	25	1061	31	2631	24	421	44	1369	29	5870	17	5273	15	8611	12	7357	15	1122	42	1482	35	2376	28	0	.	827	41	1738	29	3176	28	1873	26	1223	50	3209	25	1504	32	1524	29	1857	35	4693	24	1286	43	7015	15	2133	27	1786	26	3045	22	767	40	1151	29	694	37	774	49	740	32	1764	39	350	53	2390	30	1551	30	2599	32	2533	25	2302	36	3628	27	3327	22	1456	30	2088	26	1898	29	2669	25	1596	34	657	37	1396	36	1652	28	1204	30	1767	24	931	47	1373	28	688	47	4210	26	5368	22	8804	18	4119	25	3706	27	5713	21	1559	34	338	89	512	49	1535	36	1344	40	1184	39	863	42	402	56	2020	26	285	50	623	38	803	30	1934	33	1940	32	1135	36	1685	37	798	38	233	64	762	55	1889	29	1331	28	4889	19	560	39	983	35	824	36	2180	26	1592	26	1310	31	1134	34	744	33	0	.	192883	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	.
	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	.
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	1236	29	940	39	512	41	568	27	312	52	530	48	4713	14	3247	14	4386	17	3286	18	251	58	469	41	760	30	0	.	557	45	1092	24	1465	26	921	28	307	50	1499	25	1612	26	1473	22	1741	25	4188	17	3377	16	3196	17	1235	29	1734	26	2429	19	470	43	806	26	212	45	422	53	809	30	953	47	999	33	1083	32	649	39	1192	33	1271	28	708	32	1412	31	1707	26	670	38	742	38	771	35	648	34	798	33	1108	35	381	45	973	32	804	36	1614	35	721	46	1074	25	643	28	1982	29	1420	28	3501	22	1867	24	1199	30	1721	26	895	31	257	67	1035	34	611	38	516	34	728	40	473	65	379	60	936	27	106	58	246	50	948	27	792	39	764	38	368	41	789	47	530	39	39	100	368	63	4139	15	3076	19	3474	16	751	42	398	42	487	41	1397	35	910	47	729	38	160	37	448	45	0	.	110116	3
	16-23 cm	1083	24	1268	36	662	36	1152	27	529	41	1324	31	6068	12	4530	14	5285	14	4647	15	306	53	713	50	1006	35	35	.	901	43	2030	24	2453	23	1479	32	811	54	1932	24	2138	20	2126	24	1621	20	5295	14	5180	13	4041	17	1241	24	2109	24	3177	19	567	55	1147	33	552	38	474	39	944	31	1321	34	955	35	1557	26	814	32	1107	31	1762	28	1120	38	1563	25	1639	24	639	34	808	31	957	33	863	30	972	33	1447	32	790	38	1048	30	1104	35	1282	35	435	42	1394	23	755	29	1744	30	2249	25	3348	20	2221	23	1906	29	2404	23	1055	36	493	42	1061	32	908	29	607	43	1055	42	499	36	310	53	1370	30	368	47	272	37	983	30	973	37	1234	34	692	37	647	41	697	45	137	77	294	46	4807	15	4029	16	4293	16	658	41	378	37	495	36	1370	31	956	30	606	35	462	38	600	41	0	.	139336	2
	24-35 cm	1178	24	742	27	542	34	1105	23	867	30	1649	29	6444	12	4598	13	4866	12	4031	15	270	45	690	33	1025	28	122	.	451	43	1594	27	2274	24	1526	23	263	48	1786	34	2277	17	1481	22	1624	22	4582	14	4440	13	3778	14	1495	25	1607	22	2469	20	411	38	791	30	547	34	455	36	1002	27	1122	29	724	33	1451	25	1109	26	1125	32	1500	23	934	30	2072	23	1577	23	523	40	1150	27	532	31	1295	26	746	26	1218	28	836	27	1054	29	642	32	771	28	158	46	935	29	846	32	470	30	1295	24	1519	21	1080	24	924	28	1574	24	720	31	403	53	996	32	479	36	684	36	834	35	435	46	172	76	1159	26	446	50	343	47	993	25	659	29	980	28	776	35	433	38	412	47	163	53	440	52	3519	15	2692	17	3377	15	336	38	416	40	642	33	1231	26	1211	24	513	33	620	39	255	39	0	.	118503	2
	36-51 cm	892	20	767	27	249	37	1557	19	642	28	1103	24	4972	11	3516	11	3850	10	2348	14	259	43	322	43	514	30	14	.	378	40	950	24	1158	20	1347	24	64	81	1194	20	1498	16	995	18	1567	19	2862	13	3105	12	2786	12	1077	25	1691	19	1748	17	351	34	468	26	348	33	234	37	674	22	336	31	475	32	1055	24	695	26	645	31	742	22	574	29	1273	24	986	21	630	27	580	27	677	30	712	24	488	25	916	24	530	26	599	30	451	29	507	24	247	37	622	25	665	27	160	36	317	26	392	29	478	24	337	30	503	28	311	37	257	49	435	32	466	34	491	30	405	30	358	37	148	73	605	26	225	46	253	38	462	23	334	32	568	29	694	26	299	41	243	38	131	57	433	45	1958	15	1571	16	1921	14	353	37	360	31	745	29	996	23	844	23	517	29	458	32	443	32	0	.	79375	2
	=52 cm	315	28	332	29	127	35	442	23	172	41	245	29	1679	12	1704	12	1627	12	460	20	194	44	56	68	146	32	0	.	99	45	228	33	455	26	258	29	36	83	465	25	633	20	502	22	649	18	1165	13	951	15	752	15	415	29	530	21	535	24	110	40	141	35	107	40	101	56	298	29	112	42	126	35	341	30	241	33	191	46	333	27	322	32	596	25	274	24	260	34	85	41	130	47	100	41	302	29	254	28	267	34	172	33	188	37	299	27	180	35	326	31	204	36	86	38	102	36	89	50	263	24	96	39	294	29	161	39	107	52	146	43	231	34	145	35	128	40	162	38	0	.	215	34	46	60	113	51	115	29	157	40	93	43	182	37	37	68	138	51	46	71	138	44	866	17	480	20	770	18	79	45	143	39	170	32	288	27	263	34	171	34	154	44	130	38	0	.	28035	2
	total	4705	19	4049	25	2091	31	4824	18	2523	30	4851	24	23876	10	17596	10	20014	10	14773	13	1280	39	2250	34	3450	26	171	.	2385	34	5895	21	7804	19	5531	21	1481	47	6876	20	8158	15	6577	18	7203	16	18092	11	17054	11	14553	12	5465	20	7670	17	10357	16	1909	34	3353	24	1765	30	1687	35	3727	23	3844	30	3278	29	5486	22	3509	24	4260	26	5608	21	3657	28	6915	22	6183	19	2722	25	3366	25	3067	26	3619	24	3307	24	4943	25	2803	25	3846	25	3188	28	4473	25	1740	32	4352	19	3113	23	4442	25	5383	22	8849	18	5909	20	4462	25	6497	20	3142	28	1516	40	3673	28	2695	26	2442	29	3151	35	1927	34	1010	46	4286	23	1190	41	1227	32	3501	23	2916	27	3640	27	2712	26	2204	34	2020	37	516	55	1673	40	15288	12	11847	14	13834	13	2178	33	1695	30	2539	26	5281	22	4184	22	2535	27	1853	31	1876	30	0	.	475365	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. 2298891. Birmensdorf, Swiss Federal Research Institute WSL

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