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
total number of stems
tree state (living/dead) · diameter at breast height (10 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
tree state (living/dead)	diameter at breast height (10 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	.
	12-20 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	.
	21-30 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	.
	31-40 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	.
	41-50 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	.
	51-60 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	.
	61-70 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	.
	71-80 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	.
	81-90 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	.
	91-100 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	.
	>100 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	.
living	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	.
	12-20 cm	1931	26	1958	38	1054	39	1167	24	645	44	1336	38	8894	13	6284	13	7901	15	6185	17	521	56	1014	46	1454	31	17	.	1290	43	2469	24	3149	23	2046	30	845	50	2674	23	2927	22	2830	22	2798	22	7583	15	6965	15	6178	15	2151	26	3200	25	4477	18	869	49	1434	26	603	37	693	43	1547	29	1826	42	1579	31	2091	29	1249	34	1989	31	2371	26	1495	34	2334	28	2807	22	1187	36	1359	34	1488	35	1157	29	1533	33	2199	33	972	38	1647	31	1640	36	2519	34	1051	43	2024	22	1216	26	3221	28	3082	25	5976	20	3416	22	2660	29	3495	24	1650	30	676	48	1769	31	1319	31	918	38	1474	38	833	49	638	56	1810	27	401	49	426	42	1648	27	1398	36	1681	35	872	36	1177	43	887	38	107	71	638	53	7745	15	5757	17	6449	15	1285	41	655	40	828	38	2371	33	1582	39	1182	38	453	36	876	44	0	.	206181	2
	21-30 cm	888	26	788	28	408	40	1206	25	696	33	1560	28	6162	11	4417	14	4981	13	4495	15	217	44	626	34	937	29	87	.	449	46	1728	25	2175	25	1416	24	482	49	2057	30	2187	17	1674	21	1638	21	4832	14	4422	13	3276	16	1012	27	1598	22	2879	21	419	35	1019	31	557	33	498	43	721	28	1228	31	864	38	1401	25	931	28	1050	30	1750	23	949	37	2068	24	1552	25	507	36	868	30	549	32	1189	28	655	26	1144	32	641	30	1162	31	739	33	829	35	192	45	1097	26	739	27	841	27	1621	22	1940	20	1538	23	1147	26	1718	24	816	34	349	52	871	34	529	32	655	36	882	43	501	44	103	71	1242	28	292	49	308	46	984	26	720	31	1000	30	676	36	515	36	594	47	163	58	270	51	3339	15	3184	17	3649	15	332	44	349	43	526	37	1292	24	1121	25	423	36	530	38	292	39	0	.	119923	2
	31-40 cm	975	22	475	29	392	35	1018	20	643	30	1040	27	4017	12	2949	13	3042	12	2404	15	193	43	404	39	622	27	59	.	369	36	891	27	1338	23	1054	24	68	68	1040	28	1535	17	880	23	1082	21	2704	14	2993	13	2811	15	1247	27	1384	19	1385	20	305	37	481	34	293	38	256	39	742	27	509	31	406	35	1000	25	714	25	677	31	675	23	535	29	1133	23	1075	22	276	36	715	29	436	30	792	25	515	29	723	26	625	30	461	28	285	33	510	29	168	43	552	27	541	35	190	37	415	30	629	25	466	28	359	28	718	27	349	33	230	54	636	31	315	42	442	36	413	33	243	41	204	73	700	26	337	50	264	40	454	26	428	29	585	26	562	28	306	48	239	51	118	60	364	51	2143	16	1470	17	1908	17	255	36	258	37	588	33	751	25	755	23	416	32	482	39	271	38	0	.	73676	2
	41-50 cm	526	22	466	30	110	40	921	20	331	31	654	25	2908	12	2068	12	2276	11	1178	15	148	47	143	42	278	31	7	.	179	43	558	25	608	21	721	24	50	87	582	21	828	17	661	20	988	19	1717	13	1616	12	1427	13	613	30	917	21	983	19	192	33	278	29	191	35	130	42	404	25	170	35	274	34	595	26	365	29	334	32	433	24	288	32	764	25	461	23	451	28	324	31	430	31	360	28	303	27	617	26	286	30	362	33	337	31	296	24	122	39	331	26	388	27	103	44	155	31	208	33	205	30	200	32	265	29	160	38	154	51	244	35	285	34	254	33	227	29	189	40	64	65	304	27	107	48	116	48	272	25	205	31	259	39	413	29	168	47	162	38	66	57	249	45	1112	16	889	18	1011	16	210	39	270	32	363	30	551	25	431	26	330	30	228	32	287	34	0	.	44659	2
	51-60 cm	293	28	260	28	80	39	349	23	148	38	238	30	1427	12	1227	13	1235	13	392	20	125	44	28	50	125	32	0	.	85	46	159	32	334	27	228	27	36	83	300	27	417	21	365	21	420	19	788	14	812	16	626	15	324	28	409	22	502	23	95	38	95	40	86	47	59	54	229	29	106	41	98	35	309	31	174	34	107	38	240	30	222	35	404	22	230	23	212	39	71	40	122	43	109	37	187	30	208	30	154	34	138	38	115	39	178	30	103	45	178	27	130	30	43	46	65	40	41	51	136	30	59	50	166	33	119	44	65	48	109	38	162	38	109	34	130	41	82	47	0	.	180	34	22	58	83	50	122	29	134	40	100	47	169	39	37	68	73	45	38	67	125	46	517	20	393	22	566	18	75	40	122	35	177	34	231	28	244	35	138	35	127	47	115	39	0	.	21165	2
	61-70 cm	71	42	59	40	34	53	115	33	39	56	23	58	348	18	436	17	372	18	66	36	46	47	28	79	28	61	0	.	13	71	64	41	173	32	28	50	0	.	118	44	201	23	104	33	164	27	274	19	133	27	178	28	98	49	143	26	111	39	15	71	31	50	21	58	30	50	69	36	6	100	57	43	83	41	46	46	79	57	94	37	107	34	139	37	58	46	75	44	15	70	36	52	12	100	86	41	42	46	79	47	40	53	50	54	91	40	76	47	82	48	53	52	36	44	29	61	8	100	62	35	36	53	46	47	26	50	28	79	22	74	60	36	37	52	15	72	63	43	0	.	43	53	16	70	30	61	7	100	31	61	16	71	21	58	0	.	44	64	8	100	22	75	216	27	85	33	182	25	14	71	14	71	50	44	56	43	35	52	33	45	20	58	36	54	0	.	6485	4
	71-80 cm	21	75	35	59	13	71	35	52	20	76	0	.	84	30	130	25	146	27	37	54	23	74	0	.	7	100	0	.	0	.	13	100	29	50	15	71	0	.	69	35	56	40	54	50	70	32	156	26	85	33	42	41	0	.	19	71	18	70	15	71	15	71	7	100	21	74	8	100	0	.	0	.	6	100	20	72	18	75	24	59	60	46	54	54	0	.	14	71	14	71	8	100	0	.	22	58	11	100	23	58	28	50	7	100	42	49	7	100	36	51	38	53	7	100	8	100	24	74	30	50	0	.	41	46	22	58	7	100	15	71	7	100	13	71	0	.	16	71	0	.	7	100	15	71	0	.	15	71	0	.	0	.	0	.	0	.	15	71	16	100	7	100	159	28	40	46	48	42	7	100	21	74	7	100	20	58	17	72	0	.	7	100	0	.	0	.	2263	6
	81-90 cm	0	.	7	100	0	.	8	100	0	.	0	.	30	50	77	32	52	43	8	100	0	.	7	100	0	.	0	.	0	.	13	71	0	.	16	100	0	.	22	58	7	100	7	100	26	50	13	71	21	58	7	100	20	74	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	6	100	7	100	0	.	13	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	9	100	16	100	0	.	7	100	31	100	8	100	0	.	0	.	8	100	18	72	0	.	19	73	0	.	0	.	7	100	17	72	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	36	45	22	74	14	71	0	.	0	.	0	.	8	100	0	.	14	71	7	100	0	.	0	.	658	12
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	8	100	7	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	15	71	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	7	100	7	100	11	100	0	.	0	.	8	100	16	71	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	7	100	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	19
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	25	58	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	10	100	0	.	0	.	0	.	0	.	39	64	0	.	22	58	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	27
	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
dead	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	.
	12-20 cm	106	47	91	52	66	79	116	55	76	72	156	45	1229	18	300	40	244	30	462	28	56	58	17	100	139	39	0	.	145	71	291	50	129	43	136	39	54	73	563	34	317	31	357	39	614	32	1264	20	633	23	631	24	119	47	106	58	443	32	54	74	0	.	17	100	17	100	35	70	168	48	136	66	201	48	65	59	151	41	380	32	242	51	130	43	87	44	53	58	294	49	68	79	211	51	97	65	429	68	142	44	165	64	0	.	17	100	118	47	151	43	89	66	331	53	397	33	1096	28	149	35	196	49	580	32	66	71	18	100	189	41	176	52	113	54	102	58	125	54	155	100	322	42	75	62	0	.	19	100	121	74	104	71	200	50	116	71	69	79	0	.	153	78	1120	26	760	26	779	31	36	100	52	74	0	.	68	61	134	61	85	60	52	100	85	53	0	.	20421	5
	21-30 cm	139	50	0	.	0	.	0	.	51	58	54	57	505	30	51	58	53	58	52	57	0	.	17	100	0	.	0	.	35	71	69	50	0	.	0	.	0	.	158	39	137	43	178	59	189	40	449	26	123	43	316	36	56	58	36	72	85	45	19	100	19	100	0	.	0	.	0	.	48	58	75	50	56	59	47	73	69	62	89	60	15	100	209	43	17	100	17	100	17	100	52	74	35	71	34	71	39	71	22	100	20	100	33	71	0	.	0	.	38	71	71	61	22	100	18	100	139	42	87	65	96	44	47	72	69	71	37	100	127	43	34	71	36	71	36	100	69	81	0	.	37	71	0	.	0	.	19	100	17	100	0	.	34	71	0	.	0	.	0	.	39	100	526	32	299	31	383	30	0	.	0	.	0	.	34	71	50	58	17	100	0	.	17	100	0	.	6082	7
	31-40 cm	49	63	0	.	0	.	8	100	15	100	57	56	256	29	43	51	8	100	15	71	22	75	0	.	24	100	0	.	0	.	58	58	0	.	14	71	0	.	49	87	22	75	113	80	77	49	148	44	143	46	80	41	35	67	0	.	15	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	39	46	0	.	18	70	14	71	19	73	67	52	0	.	58	59	7	100	24	100	8	100	65	58	0	.	33	61	0	.	0	.	0	.	31	65	39	68	15	71	0	.	25	76	16	70	14	71	0	.	62	56	34	71	18	100	18	100	0	.	31	82	36	100	0	.	0	.	22	58	0	.	0	.	17	100	0	.	0	.	57	100	0	.	0	.	0	.	0	.	164	32	120	54	211	32	0	.	0	.	0	.	24	77	17	100	7	100	0	.	7	100	0	.	2596	9
	41-50 cm	14	70	7	100	0	.	0	.	14	71	7	100	159	29	7	100	14	100	7	100	0	.	0	.	7	100	0	.	0	.	7	100	6	100	0	.	0	.	7	100	29	61	35	66	75	45	80	30	15	71	21	58	0	.	7	100	7	100	18	100	0	.	0	.	0	.	8	100	19	73	32	75	13	71	0	.	0	.	22	58	6	100	7	100	0	.	14	71	7	100	7	100	0	.	6	100	9	100	15	72	0	.	0	.	0	.	7	100	42	57	7	100	0	.	0	.	16	70	8	100	8	100	8	100	7	100	7	100	0	.	7	100	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	79	0	.	0	.	0	.	15	71	98	34	56	40	77	40	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	1134	10
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	60	39	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	20	58	37	52	7	100	34	45	21	100	0	.	0	.	0	.	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	12	100	0	.	9	100	14	71	7	100	6	100	0	.	14	71	0	.	0	.	0	.	22	74	0	.	25	77	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	14	71	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	77	40	20	76	28	50	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	514	14
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	14	71	6	100	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	6	100	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	24
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	15	71	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	9	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	29
	81-90 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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50
	91-100 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	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	71
	>100 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	308	34	98	49	66	79	124	52	156	52	274	33	2223	17	401	35	319	28	536	27	78	56	35	71	178	38	0	.	180	65	425	46	158	38	150	37	54	73	819	29	542	27	704	40	995	28	1990	17	913	21	1047	24	210	41	156	49	559	29	100	61	19	100	17	100	17	100	43	60	235	42	257	42	333	38	112	59	254	34	539	27	296	48	418	35	104	40	162	42	325	45	152	55	254	44	224	46	477	65	237	39	185	58	33	71	17	100	156	41	292	34	183	54	353	50	440	31	1276	26	258	32	308	41	697	32	183	40	81	80	354	32	223	51	231	37	173	67	194	62	155	100	388	38	75	62	0	.	56	58	138	66	104	71	325	42	116	71	69	79	0	.	207	64	2020	22	1263	25	1494	23	36	100	59	67	0	.	126	41	207	49	108	54	52	100	108	47	0	.	31018	4
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	.
	12-20 cm	2037	25	2050	37	1120	38	1283	24	721	45	1493	35	10122	12	6584	13	8145	15	6647	16	576	52	1031	45	1593	30	17	.	1434	42	2760	24	3278	22	2182	29	899	51	3238	23	3244	22	3187	22	3413	21	8847	14	7598	14	6809	15	2270	25	3306	24	4921	18	923	47	1434	26	621	38	711	43	1582	29	1994	41	1715	31	2292	28	1314	33	2140	30	2750	25	1738	30	2463	27	2895	22	1240	35	1653	33	1556	35	1368	30	1630	31	2628	35	1114	38	1813	30	1640	36	2536	34	1169	39	2175	22	1306	26	3552	28	3479	25	7072	19	3565	22	2857	29	4075	23	1716	30	694	49	1958	31	1495	30	1031	37	1576	37	958	45	793	55	2132	27	476	49	426	42	1667	27	1520	35	1785	35	1072	33	1294	43	956	38	107	71	790	47	8865	14	6517	16	7228	16	1320	40	707	40	828	38	2440	33	1715	37	1266	36	505	35	961	42	0	.	226602	2
	21-30 cm	1028	25	788	28	408	40	1206	25	747	32	1614	27	6667	11	4468	14	5034	13	4548	15	217	44	644	35	937	29	87	.	484	45	1797	25	2175	25	1416	24	482	49	2214	29	2325	18	1852	21	1827	21	5281	14	4545	13	3592	15	1067	26	1634	21	2964	21	438	35	1038	31	557	33	498	43	721	28	1276	30	939	36	1457	25	979	27	1118	30	1839	22	964	36	2276	23	1569	25	524	35	885	30	601	31	1224	28	689	25	1183	32	663	29	1181	30	772	32	829	35	192	45	1134	26	810	26	863	27	1638	22	2079	20	1625	23	1243	26	1764	24	885	32	386	50	997	31	563	31	691	35	918	43	569	46	103	71	1278	28	292	49	308	46	1003	27	737	32	1000	30	710	35	515	36	594	47	163	58	309	56	3865	15	3483	16	4033	15	332	44	349	43	526	37	1326	25	1171	25	440	35	530	38	308	37	0	.	126005	2
	31-40 cm	1024	22	475	29	392	35	1025	20	658	30	1097	27	4273	12	2992	13	3050	12	2419	15	215	44	404	39	647	27	59	.	369	36	949	27	1338	23	1068	23	68	68	1088	27	1557	17	993	22	1159	21	2852	14	3136	13	2891	15	1282	27	1384	19	1401	20	314	37	481	34	293	38	256	39	742	27	509	31	406	35	1039	25	714	25	694	31	689	23	554	29	1200	23	1075	22	334	35	722	29	460	31	799	25	580	28	723	26	658	29	461	28	285	33	510	29	199	43	591	27	556	34	190	37	441	30	645	25	480	27	359	28	780	25	383	31	249	51	654	31	315	42	473	36	449	35	243	41	204	73	723	26	337	50	264	40	471	26	428	29	585	26	619	29	306	48	239	51	118	60	364	51	2307	15	1590	17	2120	16	255	36	258	37	588	33	775	25	772	23	423	32	482	39	277	38	0	.	76272	2
	41-50 cm	540	22	473	29	110	40	921	20	344	30	661	25	3067	12	2075	12	2290	11	1185	15	148	47	143	42	285	31	7	.	179	43	564	25	614	21	721	24	50	87	589	21	858	17	696	20	1064	18	1797	13	1631	12	1448	13	613	30	924	21	991	19	210	31	278	29	191	35	130	42	412	24	189	36	305	35	607	26	365	29	334	32	455	23	294	32	771	25	461	23	465	28	330	30	437	31	360	28	309	27	626	26	301	30	362	33	337	31	296	24	129	38	373	27	395	27	103	44	155	31	224	33	213	29	208	31	273	29	167	37	161	49	244	35	293	33	284	35	227	29	189	40	64	65	304	27	107	48	116	48	272	25	205	31	259	39	440	29	168	47	162	38	66	57	264	44	1210	16	945	17	1088	15	210	39	270	32	363	30	551	25	438	26	330	30	228	32	287	34	0	.	45793	2
	51-60 cm	293	28	260	28	80	39	349	23	148	38	238	30	1487	12	1227	13	1235	13	392	20	125	44	28	50	132	33	0	.	85	46	159	32	334	27	228	27	36	83	321	27	454	20	372	21	454	19	809	14	812	16	626	15	324	28	416	22	511	22	95	38	95	40	86	47	59	54	229	29	106	41	105	35	321	30	174	34	116	36	254	29	229	35	410	22	230	23	226	37	71	40	122	43	109	37	209	31	208	30	179	34	138	38	115	39	178	30	103	45	193	29	130	30	43	46	65	40	49	45	136	30	59	50	166	33	126	42	65	48	123	38	162	38	116	35	130	41	82	47	0	.	187	33	22	58	83	50	122	29	134	40	100	47	175	40	37	68	73	45	38	67	125	46	595	19	413	22	594	18	75	40	129	35	177	34	231	28	244	35	138	35	127	47	115	39	0	.	21679	2
	61-70 cm	71	42	59	40	34	53	115	33	39	56	23	58	363	17	436	17	372	18	66	36	46	47	28	79	28	61	0	.	13	71	64	41	180	31	28	50	0	.	125	43	201	23	118	32	171	27	288	19	133	27	178	28	98	49	143	26	111	39	15	71	31	50	21	58	30	50	69	36	6	100	64	39	89	40	46	46	79	57	100	35	107	34	139	37	58	46	82	41	15	70	36	52	12	100	86	41	42	46	79	47	40	53	50	54	91	40	76	47	82	48	53	52	36	44	29	61	8	100	62	35	36	53	46	47	26	50	28	79	22	74	66	36	37	52	15	72	63	43	0	.	43	53	16	70	30	61	7	100	31	61	16	71	21	58	0	.	44	64	8	100	22	75	230	27	93	31	189	25	14	71	14	71	50	44	56	43	35	52	33	45	20	58	36	54	0	.	6610	4
	71-80 cm	21	75	35	59	13	71	35	52	20	76	0	.	84	30	130	25	146	27	37	54	23	74	0	.	7	100	0	.	0	.	13	100	45	48	15	71	0	.	83	34	56	40	54	50	70	32	170	25	85	33	42	41	0	.	19	71	18	70	15	71	15	71	7	100	21	74	8	100	0	.	0	.	13	71	20	72	27	60	31	51	60	46	54	54	0	.	14	71	14	71	8	100	0	.	22	58	11	100	23	58	28	50	7	100	42	49	7	100	36	51	38	53	7	100	8	100	24	74	30	50	0	.	41	46	22	58	7	100	22	74	7	100	28	62	0	.	16	71	0	.	7	100	15	71	0	.	15	71	0	.	0	.	0	.	0	.	15	71	16	100	7	100	166	29	40	46	56	39	7	100	21	74	7	100	20	58	17	72	0	.	7	100	0	.	0	.	2366	6
	81-90 cm	0	.	7	100	0	.	8	100	0	.	0	.	30	50	77	32	52	43	8	100	0	.	7	100	0	.	0	.	0	.	13	71	0	.	16	100	0	.	22	58	7	100	7	100	26	50	13	71	21	58	7	100	20	74	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	6	100	14	71	0	.	13	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	9	100	16	100	0	.	7	100	38	83	8	100	0	.	0	.	8	100	18	72	8	100	19	73	0	.	0	.	7	100	17	72	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	43	41	22	74	14	71	0	.	0	.	0	.	8	100	0	.	14	71	7	100	0	.	0	.	687	12
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	8	100	7	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	15	71	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	14	71	6	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	7	100	7	100	11	100	0	.	0	.	8	100	16	71	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	7	100	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	231	18
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	25	58	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	10	100	0	.	0	.	0	.	0	.	39	64	0	.	22	58	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	27
	total	5013	18	4147	25	2157	31	4948	18	2678	30	5125	24	26099	10	17997	10	20333	10	15308	13	1358	38	2285	34	3628	25	171	.	2565	35	6320	21	7962	19	5681	21	1535	48	7695	21	8701	15	7280	17	8198	16	20081	11	17967	11	15600	12	5674	19	7826	17	10916	16	2009	34	3372	24	1782	30	1704	35	3769	23	4079	30	3535	28	5819	22	3621	24	4514	26	6147	21	3953	27	7333	21	6287	19	2884	25	3691	25	3219	26	3872	25	3531	24	5420	26	3040	25	4031	25	3221	27	4490	25	1896	30	4644	19	3296	24	4795	25	5823	22	10125	17	6167	19	4770	25	7194	20	3325	28	1597	40	4028	27	2918	25	2673	28	3324	35	2121	35	1165	46	4674	23	1265	42	1227	32	3557	23	3054	28	3744	27	3038	26	2320	34	2089	38	516	55	1880	40	17309	12	13110	14	15328	13	2213	33	1755	31	2539	26	5407	23	4392	22	2644	27	1905	31	1984	30	0	.	506383	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. 2292523. Birmensdorf, Swiss Federal Research Institute WSL

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