Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
volume of deadwood (stemwood)
diameter at breast height (10 classes) · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
diameter at breast height (10 classes)	conifers and broadleaves	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
12-20 cm	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	.
	conifers	9	45	11	54	6	88	11	44	9	41	23	31	192	16	75	22	61	24	47	23	2	71	7	67	6	47	0	.	14	54	34	41	43	34	16	44	0	.	46	29	45	20	32	31	68	21	118	18	141	17	62	22	28	40	20	34	63	29	4	71	2	71	5	58	0	0	28	31	10	39	24	34	27	31	23	46	12	55	39	26	10	50	19	37	14	40	3	72	12	51	3	73	15	43	12	38	30	43	19	43	6	67	1	100	9	55	9	35	30	34	13	39	0	0	6	59	2	100	14	40	10	57	7	50	8	44	3	83	41	32	20	34	8	61	28	32	11	40	4	62	20	29	7	84	10	54	15	39	23	43	16	39	10	46	5	71	3	62	1	100	4	59	160	14	115	19	150	19	9	45	10	57	3	58	10	44	3	80	5	59	2	100	9	54	0	.	2321	4
	broadleaves	25	40	7	58	2	77	10	35	10	54	9	60	79	24	16	31	36	37	27	30	4	80	16	51	17	35	0	.	9	84	10	51	7	46	23	41	9	72	20	35	18	30	12	56	46	28	88	22	13	51	50	21	19	53	12	43	46	34	10	39	4	61	4	64	1	100	10	53	9	63	19	59	16	48	3	77	16	41	18	40	15	36	19	36	18	36	0	0	11	51	10	77	19	42	17	40	3	71	10	52	9	46	6	48	4	71	29	38	37	48	29	41	63	30	64	30	231	20	85	27	68	35	103	24	2	78	7	82	5	62	31	40	26	35	21	28	5	69	8	72	9	61	5	60	8	55	4	71	8	58	13	44	8	42	5	55	3	75	1	100	6	64	39	29	30	37	80	21	5	68	2	100	8	43	9	64	10	59	4	50	2	100	4	71	0	.	1965	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	1	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	.	1	100	0	.	0	.	3	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	.	0	.	0	.	0	.	0	.	0	.	0	.	5	68
	total	34	34	18	40	8	66	21	29	19	38	33	27	271	14	91	19	97	21	74	19	6	60	23	41	23	31	0	.	23	58	44	38	50	30	39	32	9	72	66	24	62	17	44	27	114	17	206	14	154	17	111	16	46	32	32	27	110	22	14	35	6	54	9	43	1	100	39	29	19	38	44	33	43	26	26	42	28	35	57	22	25	30	39	27	32	27	4	59	23	36	13	64	36	33	29	29	33	40	29	36	15	39	7	43	13	44	38	31	66	30	42	32	63	30	69	28	233	20	99	24	77	32	109	23	10	39	10	63	46	29	52	30	34	31	49	26	16	40	11	53	29	31	12	55	18	39	20	38	31	38	29	30	18	32	11	49	6	48	3	71	10	50	199	13	145	17	230	14	14	39	12	50	12	35	20	41	13	48	9	41	3	71	12	44	0	.	4291	3
21-30 cm	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	.
	conifers	13	71	0	0	10	100	5	100	31	48	76	33	415	16	102	25	77	35	79	28	13	100	9	100	26	51	0	.	0	0	39	39	90	33	60	42	0	0	72	33	81	33	75	35	138	24	327	18	167	22	100	31	38	39	25	52	212	34	7	100	12	74	4	100	0	0	12	71	29	43	44	36	20	54	27	58	10	79	66	32	8	100	34	46	11	58	10	71	20	53	19	72	31	42	30	52	83	48	3	100	3	100	16	60	0	0	15	66	37	37	28	54	6	100	16	72	0	0	5	72	6	71	9	74	17	64	7	100	56	31	9	73	14	65	49	40	30	64	5	100	28	54	17	100	13	71	26	48	15	60	7	72	13	100	6	100	24	88	0	0	4	100	314	18	148	21	217	19	18	50	0	0	18	72	13	73	0	0	3	100	11	71	4	100	0	.	3985	5
	broadleaves	28	50	15	74	0	0	10	73	0	0	13	62	52	27	28	39	39	38	28	51	3	100	15	71	28	58	0	.	9	71	7	100	7	72	3	100	15	60	41	35	14	52	13	53	18	56	55	37	2	100	80	29	18	60	11	60	26	49	5	100	3	100	0	0	0	0	0	0	6	100	0	0	21	49	16	84	28	55	33	44	12	62	10	73	0	0	3	100	7	100	20	79	21	57	14	68	6	71	25	52	3	100	0	0	3	100	26	56	5	73	10	66	23	54	82	30	134	23	75	37	32	49	63	31	3	100	0	0	0	0	11	71	3	100	2	100	0	0	3	100	26	51	10	71	8	76	0	0	9	75	4	100	21	56	0	0	8	100	0	0	4	100	25	41	25	45	36	35	0	0	3	100	3	100	32	46	14	51	9	72	0	0	12	71	0	.	1535	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	total	41	41	15	74	10	100	16	59	31	48	88	30	468	15	130	24	116	27	108	26	16	84	24	58	54	40	0	.	9	71	46	36	97	31	63	41	15	60	113	27	95	30	88	31	156	22	382	16	169	22	180	22	55	33	36	41	237	31	12	72	15	62	4	100	0	0	12	71	35	40	44	36	41	37	42	48	38	46	99	27	20	54	44	42	11	58	13	59	27	47	39	58	52	34	44	42	89	46	28	48	6	71	16	60	3	100	41	43	42	33	38	54	29	48	98	27	134	23	80	35	38	43	72	28	19	57	7	100	56	31	21	66	16	56	51	38	30	64	7	73	55	37	26	85	21	53	26	48	24	60	11	59	34	52	6	100	33	70	0	0	8	71	338	17	174	20	253	17	22	45	3	100	21	63	45	39	14	51	12	59	11	71	16	59	0	.	5523	4
31-40 cm	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	.
	conifers	10	100	12	72	0	0	18	73	8	100	55	43	649	15	111	29	67	48	88	37	16	76	0	0	36	52	0	.	11	100	54	47	81	43	64	40	0	0	50	50	88	29	127	26	216	29	301	21	158	23	131	27	44	54	32	59	128	32	0	0	0	0	31	62	0	0	6	100	26	55	46	45	25	52	19	75	34	67	138	39	47	63	18	63	12	71	23	71	5	100	0	0	59	37	75	37	40	47	76	48	0	0	9	100	0	0	28	63	17	59	10	72	4	100	8	100	0	0	22	59	0	0	14	58	21	51	21	81	52	42	33	56	47	42	37	47	33	53	0	0	17	75	0	0	5	100	39	47	11	72	0	0	23	62	0	0	12	100	0	0	11	71	266	19	158	24	354	18	29	71	5	100	32	53	54	43	33	58	23	71	0	0	0	0	0	.	4667	5
	broadleaves	20	58	12	71	9	71	17	73	4	100	22	58	66	37	21	65	19	64	28	57	10	71	6	100	15	58	0	.	8	71	5	100	9	100	21	62	0	0	4	100	0	0	8	71	21	78	36	48	3	100	37	52	4	100	0	0	33	71	0	0	0	0	0	0	0	0	0	0	17	66	0	0	0	0	0	0	4	100	40	44	19	61	14	76	7	100	6	100	60	66	0	0	10	71	30	81	6	100	3	100	4	100	0	0	0	0	17	100	6	100	22	60	0	0	63	42	46	46	45	41	28	43	23	63	4	100	0	0	0	0	8	100	0	0	0	0	7	100	0	0	5	100	12	74	0	0	0	0	21	79	5	100	6	71	0	0	0	0	0	0	18	80	4	100	6	100	23	51	6	100	0	0	7	100	40	66	7	100	0	0	0	0	7	100	0	.	1093	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	31	51	24	50	9	71	34	51	13	74	78	39	715	14	132	28	86	40	116	31	27	54	6	100	50	43	0	.	19	80	60	44	90	40	85	35	0	0	54	46	88	29	135	25	237	28	336	20	161	23	168	24	48	51	32	59	161	31	0	0	0	0	31	62	0	0	6	100	43	42	46	45	25	52	19	75	38	60	178	32	66	48	32	48	19	58	29	60	65	61	0	0	69	37	106	41	46	43	80	47	4	100	9	100	0	0	45	54	24	51	32	47	4	100	71	42	46	46	67	35	28	43	37	45	25	46	21	81	52	42	41	49	47	42	37	47	40	47	0	0	22	62	12	74	5	100	39	47	32	57	5	100	30	51	0	0	12	100	0	0	28	57	270	19	164	23	378	17	35	61	5	100	38	47	94	37	40	51	23	71	0	0	7	100	0	.	5760	4
41-50 cm	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	.
	conifers	8	100	10	100	7	100	71	46	17	71	63	47	563	16	169	26	64	38	64	36	15	72	0	0	25	58	0	.	44	74	46	42	133	32	28	58	11	100	88	43	95	33	124	40	140	26	290	21	185	21	122	31	10	100	52	53	197	32	31	61	0	0	9	100	0	0	14	71	37	66	84	41	83	43	35	51	0	0	91	33	6	100	47	66	19	71	9	100	23	58	23	74	34	51	38	52	29	50	34	51	20	71	0	0	0	0	22	50	93	41	21	59	0	0	12	100	0	0	31	45	0	0	6	100	35	63	14	100	32	52	28	45	43	48	106	41	40	60	0	0	79	48	0	0	0	0	21	74	0	0	30	61	59	48	10	100	0	0	7	100	20	100	295	18	136	27	240	22	14	100	38	62	28	58	24	58	7	100	11	100	0	0	16	71	0	.	4824	5
	broadleaves	47	53	5	100	7	100	18	72	0	0	0	0	31	54	30	51	48	42	50	71	7	100	8	100	35	53	0	.	13	72	0	0	10	100	14	71	0	0	11	71	7	100	10	71	0	0	6	100	0	0	45	41	10	71	8	100	26	100	0	0	0	0	0	0	0	0	0	0	10	100	7	100	21	72	0	0	29	47	32	49	0	0	0	0	0	0	8	100	0	0	13	71	22	76	33	60	0	0	0	0	0	0	0	0	0	0	0	0	4	100	4	100	20	51	33	59	14	100	13	58	16	71	37	39	0	0	0	0	0	0	11	100	0	0	10	72	0	0	6	100	9	100	0	0	0	0	0	0	14	100	18	100	0	0	16	71	6	100	0	0	13	72	10	71	0	0	9	78	0	0	0	0	16	71	12	100	22	58	0	0	7	100	12	100	0	.	956	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	55	47	14	75	14	71	89	40	17	71	63	47	594	15	200	25	113	28	114	37	23	58	8	100	59	47	0	.	57	59	46	42	142	31	42	45	11	100	99	39	102	31	134	37	140	26	296	21	185	21	167	25	20	61	60	47	223	31	31	61	0	0	9	100	0	0	14	71	46	56	91	41	105	37	35	51	29	47	123	30	6	100	47	66	19	71	17	71	23	58	37	54	56	43	71	39	29	50	34	51	20	71	0	0	0	0	22	50	97	40	25	52	20	51	45	51	14	100	44	40	16	71	43	36	35	63	14	100	32	52	40	43	43	48	116	40	40	60	6	100	88	44	0	0	0	0	21	74	14	100	48	53	59	48	27	58	6	100	7	100	34	67	305	18	136	27	249	21	14	100	38	62	44	45	36	51	29	50	11	100	7	100	28	59	0	.	5781	4
51-60 cm	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	.
	conifers	37	58	26	71	0	0	15	100	0	0	51	78	417	20	156	38	68	45	48	50	0	0	0	0	0	0	0	.	0	0	51	71	244	44	48	50	0	.	75	47	76	64	155	33	170	27	187	27	169	25	31	71	23	71	44	59	58	53	7	100	0	0	0	0	0	0	15	100	0	0	38	58	38	58	11	100	41	61	104	46	16	100	52	45	0	0	0	0	0	0	0	0	0	0	28	71	43	50	13	100	0	0	14	100	0	0	8	100	23	76	35	50	0	0	10	100	0	0	10	100	0	0	18	71	11	100	23	71	63	52	44	50	12	100	56	56	0	0	0	0	73	61	16	100	0	0	0	0	0	0	13	100	47	50	0	0	0	0	11	100	0	0	183	28	114	29	201	30	40	73	16	100	14	100	23	71	12	100	0	0	0	0	13	100	0	.	3659	7
	broadleaves	27	100	32	58	8	100	0	0	0	0	0	0	0	0	8	100	11	100	0	0	19	72	24	71	0	0	0	.	9	100	0	0	0	0	11	100	15	100	0	0	0	0	0	0	0	0	0	0	0	0	13	100	9	100	0	0	11	100	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	6	100	0	0	15	100	22	71	0	0	0	0	0	0	0	0	17	71	0	0	10	100	0	0	13	100	13	100	5	100	8	100	0	0	0	0	15	71	0	0	18	60	0	0	12	100	0	0	0	0	0	.	0	0	11	100	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	15	77	0	0	15	100	0	0	11	100	9	100	0	0	0	0	10	100	12	100	22	71	0	0	0	0	0	0	0	0	0	0	0	.	457	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	total	64	54	58	45	8	100	15	100	0	0	51	78	417	20	164	37	79	41	48	50	19	72	24	71	0	0	0	.	9	100	51	71	244	44	59	45	15	100	75	47	76	64	155	33	170	27	187	27	169	25	45	58	32	58	44	59	69	48	7	100	0	0	0	0	0	0	15	100	0	0	38	58	38	58	11	100	41	61	118	42	16	100	66	41	22	71	0	0	0	0	0	0	0	0	45	52	43	50	23	71	0	0	26	71	13	100	13	73	31	62	35	50	0	0	25	78	0	0	29	53	0	0	30	59	11	100	23	71	63	52	44	50	23	71	56	56	0	0	0	0	73	61	16	100	0	0	0	0	0	0	13	100	61	42	0	0	15	100	11	100	11	100	192	27	114	29	201	30	50	77	29	71	37	58	23	71	12	100	0	0	0	0	13	100	0	.	4124	6
61-70 cm	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	.
	conifers	20	100	0	0	15	100	0	0	14	100	0	0	186	36	78	51	17	100	0	0	0	0	0	0	0	0	0	.	0	0	15	100	67	62	0	0	0	.	15	100	51	58	36	58	122	53	174	28	15	100	21	100	17	100	46	71	145	36	0	0	16	100	0	0	0	0	0	0	0	0	28	71	0	0	0	0	15	100	58	60	17	100	20	100	0	0	0	0	0	0	0	0	33	100	14	100	59	50	14	100	0	0	0	0	0	0	10	100	42	58	19	100	0	0	0	0	0	0	14	100	11	100	0	0	30	71	0	0	13	100	13	100	0	0	37	100	0	0	18	100	0	0	0	0	16	100	0	0	0	0	0	0	0	0	0	0	0	0	18	100	0	0	151	35	10	100	53	58	0	0	0	0	0	0	16	100	0	0	0	0	0	0	0	0	0	.	1797	10
	broadleaves	0	0	19	100	26	100	0	0	0	0	0	0	0	0	0	0	28	72	12	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	16	100	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	21	100	0	0	0	0	0	0	17	100	0	0	0	0	0	0	0	0	0	0	17	100	0	.	0	0	0	0	0	0	0	0	0	0	18	74	0	0	20	100	9	100	7	100	0	0	0	0	19	71	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	15	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	242	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	20	100	19	100	41	73	0	0	14	100	0	0	186	36	78	51	45	58	12	100	0	0	0	0	0	0	0	.	0	0	15	100	67	62	0	0	0	0	15	100	51	58	36	58	122	53	174	28	15	100	21	100	32	71	46	71	145	36	0	0	16	100	0	0	0	0	0	0	0	0	28	71	0	0	21	100	15	100	58	60	17	100	37	71	0	0	0	0	0	0	0	0	33	100	31	71	59	50	14	100	0	0	0	0	0	0	10	100	60	46	19	100	20	100	9	100	7	100	14	100	11	100	19	71	30	71	0	0	13	100	13	100	0	0	37	100	0	0	18	100	0	0	0	0	16	100	0	0	0	0	0	0	0	0	0	0	0	0	18	100	0	0	151	35	10	100	68	48	0	0	0	0	0	0	16	100	0	0	0	0	0	0	0	0	0	.	2039	10
71-80 cm	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	.
	conifers	0	0	0	0	0	0	0	0	0	0	0	0	48	58	0	0	25	100	15	100	0	.	0	0	0	0	0	.	0	0	26	100	152	77	0	0	0	0	48	71	0	0	0	0	36	71	48	58	0	0	0	0	27	100	0	0	39	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	30	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	37	100	0	0	0	0	0	0	0	0	0	0	29	100	20	100	0	.	0	.	0	.	14	100	0	0	19	100	0	0	21	100	22	100	16	100	36	71	0	0	44	71	0	.	0	0	0	0	0	0	0	0	26	100	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	134	61	0	0	0	0	0	0	0	0	24	100	0	0	0	0	0	0	0	.	934	20
	broadleaves	0	0	0	0	0	0	0	0	0	.	0	0	0	0	24	100	0	0	0	0	0	0	0	0	0	0	0	.	0	0	15	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	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	9	100	9	100	0	0	0	0	9	100	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	15	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	23	100	0	0	0	.	104	41
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	48	58	24	100	25	100	15	100	0	0	0	0	0	0	0	.	0	0	41	73	152	77	0	0	0	0	48	71	0	0	0	0	36	71	48	58	0	0	0	0	27	100	0	0	39	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	30	100	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	37	100	0	0	0	0	0	0	0	0	0	0	38	79	29	76	0	0	0	0	9	100	14	100	0	0	19	100	0	0	21	100	22	100	16	100	36	71	0	0	59	72	0	.	0	0	0	0	0	0	0	0	26	100	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	134	61	0	0	0	0	0	0	0	0	24	100	0	0	23	100	0	0	0	.	1039	19
81-90 cm	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	.
	conifers	0	.	0	.	0	0	0	0	0	.	0	0	23	100	51	71	25	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	34	71	23	100	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	0	25	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	15	100	0	0	21	100	0	.	0	.	15	100	40	100	0	0	0	.	0	0	0	.	0	0	30	100	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	39	71	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	342	27
	broadleaves	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	22	100	0	.	24	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	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	11	100	0	.	18	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	.	75	52
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	23	100	51	71	48	71	0	0	24	100	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	34	71	23	100	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	0	25	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	15	100	0	0	21	100	0	0	0	0	26	72	40	100	18	100	0	.	0	0	0	0	0	0	30	100	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	39	71	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	418	24
91-100 cm	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	.
	conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	29	100	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	26	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	26	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	48	100	0	.	0	.	0	0	0	0	0	.	0	0	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	62	58	39	71	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	265	33
	broadleaves	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	14	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	.	0	.	14	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	29	100	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	26	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	26	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	.	14	100	0	0	0	0	48	100	0	0	0	.	0	0	0	0	0	.	0	0	0	.	35	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	62	58	39	71	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	279	32
>100 cm	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	.
	conifers	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	26	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	.	24	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	.	50	71
	broadleaves	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	26	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	.	24	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	.	50	71
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	.
	conifers	98	38	59	41	38	66	121	33	79	36	267	32	2494	12	743	16	406	20	371	22	47	61	16	68	92	34	0	.	69	51	265	31	809	33	217	28	11	100	420	25	436	21	548	20	890	18	1479	13	857	14	468	19	186	32	220	28	842	23	50	48	30	60	49	51	0	0	75	36	102	38	289	31	193	32	145	38	113	42	523	25	131	40	189	42	58	32	45	60	60	37	45	64	172	30	198	27	321	33	159	38	29	52	40	61	9	55	91	47	286	23	145	30	31	73	51	77	2	100	125	33	114	81	97	59	121	39	90	51	277	29	164	37	190	41	312	33	192	39	26	72	216	42	39	70	45	51	102	39	75	45	67	40	152	45	22	67	39	87	38	79	40	56	1468	12	721	16	1348	14	111	40	69	53	95	33	140	34	79	45	42	54	12	63	42	44	0	.	22845	3
	broadleaves	147	38	90	48	52	87	55	37	14	48	44	40	227	20	128	28	202	25	145	39	68	60	70	48	95	34	0	.	48	45	36	58	33	44	72	34	40	61	77	31	39	32	43	38	86	35	185	24	19	53	225	19	75	36	31	47	142	43	15	49	7	54	4	64	1	100	10	53	42	48	26	53	58	38	39	66	78	42	128	33	46	38	75	40	47	45	17	61	78	61	44	50	72	33	129	49	14	58	48	49	16	44	18	78	20	79	77	48	88	40	75	38	127	27	280	29	440	18	248	22	144	29	276	21	8	58	7	82	5	62	62	39	40	38	33	33	27	62	17	62	49	46	27	54	16	62	4	71	51	61	39	53	50	39	22	55	32	65	1	100	51	66	86	36	61	43	163	23	21	57	17	76	57	38	93	50	53	43	13	51	32	77	34	60	0	.	6442	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	1	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	.	8	100	0	.	0	.	0	.	1	100	0	.	0	.	3	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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	58
	total	244	28	149	34	90	57	175	25	93	32	312	30	2721	11	870	15	607	16	516	21	115	44	85	41	187	29	0	.	118	44	302	29	842	32	289	25	50	52	496	22	475	20	591	19	975	17	1664	12	875	14	693	15	261	26	251	27	984	22	64	41	37	50	53	47	1	100	86	33	143	30	315	29	251	27	185	34	190	30	659	22	177	34	264	33	105	28	63	46	138	38	88	44	247	25	327	29	336	32	207	31	45	43	58	49	29	57	168	34	374	20	219	25	157	26	331	29	443	18	373	19	258	39	373	23	129	37	97	48	282	29	226	30	229	35	345	31	219	40	43	50	266	36	66	52	60	41	106	38	126	38	105	32	202	36	44	45	71	56	39	77	90	46	1554	12	782	15	1512	13	135	39	87	45	152	26	233	31	132	33	55	43	45	59	76	41	0	.	29304	3

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. 2293185. Birmensdorf, Swiss Federal Research Institute WSL

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