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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI3
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 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	12	55	10	61	7	65	11	51	8	50	23	34	166	14	79	21	54	26	54	22	2	75	3	77	9	47	3	.	14	51	44	35	45	35	14	40	0	.	35	29	42	23	31	27	62	20	113	18	140	18	59	24	26	43	14	44	60	28	2	100	2	59	6	60	0	0	15	41	15	36	23	41	22	60	17	52	5	51	43	26	11	54	22	37	8	41	8	59	13	52	1	100	12	53	17	48	33	49	16	43	4	58	8	59	10	92	7	41	23	37	10	41	0	0	5	71	3	100	10	45	4	62	5	48	6	52	1	100	35	43	12	47	6	54	26	36	9	51	12	70	29	32	3	72	8	70	8	49	10	55	6	43	8	61	4	100	3	100	0	0	4	71	147	17	76	22	101	21	8	51	5	60	9	65	14	45	7	56	1	100	0	0	7	48	0	.	2086	4
	broadleaves	10	47	11	38	1	100	12	36	11	42	5	48	58	24	9	34	17	36	25	38	1	100	17	53	14	39	0	.	9	64	7	48	10	57	18	48	18	59	25	45	25	30	20	55	31	37	65	26	10	46	37	30	2	76	3	59	28	31	0	0	3	71	2	73	3	100	4	71	0	100	18	61	12	38	4	67	31	44	21	47	16	34	15	44	7	41	1	100	9	49	10	52	15	49	12	46	8	42	11	58	3	72	4	51	2	74	14	48	28	56	17	62	45	32	71	27	166	21	65	24	39	35	99	25	3	60	7	71	3	73	28	46	12	38	25	31	1	71	10	81	4	48	6	59	7	61	5	63	2	73	5	54	8	62	1	72	3	78	0	0	3	72	32	30	21	33	51	23	7	65	1	100	2	100	9	58	5	76	6	60	0	0	3	71	0	.	1524	5
	indeterminable	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	76
	total	22	37	22	35	9	57	23	31	19	38	28	30	225	13	88	20	71	22	78	19	3	62	20	48	23	31	3	.	23	47	51	32	55	30	32	34	18	59	60	25	67	18	51	27	93	19	178	15	150	17	96	18	28	40	17	38	89	21	2	100	5	58	8	54	3	100	19	35	16	35	41	35	34	42	21	44	36	39	65	23	28	32	37	29	15	32	9	54	21	37	11	49	27	38	28	34	41	40	27	35	8	46	12	43	13	76	20	35	51	35	27	42	45	32	76	26	168	21	75	22	42	32	104	24	9	44	8	66	38	40	40	38	18	31	51	28	10	50	22	53	34	30	9	46	15	48	13	38	12	48	12	34	16	45	5	90	6	67	0	0	7	51	180	16	98	19	152	17	15	48	6	59	11	56	23	37	12	46	7	53	0	0	10	49	0	.	3612	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	34	46	0	0	18	64	24	52	8	71	49	49	385	17	86	32	50	36	54	32	0	0	5	100	19	58	0	.	3	100	26	57	68	34	32	47	0	0	65	32	57	37	128	42	94	23	237	21	132	26	95	36	23	62	8	100	146	33	7	100	8	71	3	100	0	0	0	0	35	45	31	39	10	58	50	65	16	63	54	39	9	100	67	37	15	74	17	58	20	66	11	100	29	46	24	51	76	53	8	71	12	74	13	72	0	0	6	71	32	39	8	71	6	100	16	82	0	0	8	59	7	75	5	100	15	62	9	100	56	36	17	61	12	65	34	49	14	100	5	100	8	71	14	100	5	100	17	52	13	58	6	71	9	73	0	0	5	100	0	0	10	100	225	23	74	30	191	19	4	100	0	0	20	71	6	100	12	73	5	100	0	0	0	0	0	.	3237	5
	broadleaves	14	58	16	58	0	0	9	71	0	0	16	58	37	33	24	43	17	45	19	58	3	100	5	100	38	56	0	.	8	100	15	61	9	72	8	58	13	71	27	41	18	42	9	59	24	49	34	42	0	0	59	35	11	72	11	59	25	46	0	0	0	0	0	0	0	0	0	0	4	100	0	0	8	72	3	100	16	72	26	51	9	100	22	62	4	100	6	100	3	100	5	100	4	100	11	72	2	100	16	65	0	0	7	73	7	73	5	71	8	100	2	100	12	63	92	29	72	29	52	39	3	100	62	36	0	0	0	0	0	0	7	72	14	70	13	60	15	79	0	0	10	71	6	100	0	0	0	0	3	100	8	74	22	50	0	0	12	75	0	0	9	73	15	54	14	46	32	38	2	100	0	0	0	0	20	50	20	52	3	100	0	0	4	100	0	.	1156	7
	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	47	42	16	58	18	64	33	43	8	71	65	39	423	16	110	28	66	31	73	29	3	100	10	71	57	44	0	.	11	77	41	43	77	31	40	45	13	71	92	27	74	30	136	39	117	21	271	19	132	26	154	26	33	48	19	55	170	29	7	100	8	71	3	100	0	0	0	0	39	47	31	39	18	45	54	61	32	48	80	31	18	71	89	31	20	62	23	50	23	58	16	76	32	42	35	42	78	52	24	49	12	74	19	54	7	73	11	50	40	37	10	61	18	54	109	33	72	29	60	35	10	61	68	34	15	62	9	100	56	36	24	49	26	48	47	39	29	88	5	100	19	50	20	100	5	100	17	52	16	51	14	52	31	42	0	0	16	60	0	0	18	63	240	21	88	26	223	18	6	73	0	0	20	71	26	45	32	42	8	72	0	0	4	100	0	.	4393	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	71	84	18	74	0	0	47	56	18	100	69	38	621	17	118	26	22	61	64	36	25	71	0	0	15	58	0	.	46	52	62	39	100	43	63	39	0	0	69	41	112	24	108	29	177	31	215	27	130	29	110	29	38	62	26	59	133	48	0	0	0	0	0	0	0	0	7	100	30	55	49	52	59	43	76	54	40	57	89	44	32	70	23	60	7	100	34	72	10	71	14	73	32	52	55	44	52	43	61	47	0	0	104	100	5	100	22	64	14	62	35	80	0	0	20	72	7	100	7	100	0	0	0	0	11	72	30	77	39	50	30	59	39	49	52	49	17	78	0	0	30	65	0	0	0	0	5	100	0	0	0	0	24	82	0	0	0	0	0	0	7	100	258	20	122	29	287	21	4	100	0	0	17	72	43	43	27	52	7	100	40	76	0	0	0	.	4452	6
	broadleaves	19	58	11	72	12	73	0	0	0	0	21	59	29	50	15	58	21	59	30	48	0	0	5	100	28	58	0	.	8	71	7	100	7	71	0	0	0	0	15	58	5	100	10	72	12	100	27	48	0	0	42	50	0	0	0	0	20	100	0	0	0	0	0	0	0	0	0	0	13	74	0	0	3	100	0	0	3	100	16	52	18	71	14	77	6	100	0	0	4	100	0	0	12	71	20	86	0	0	16	71	0	0	6	100	4	100	0	0	0	0	9	72	15	73	32	45	49	51	21	63	8	100	41	66	3	100	6	100	0	0	7	71	0	0	4	100	8	100	0	0	8	100	0	0	7	100	0	0	0	0	0	0	22	85	10	100	0	0	0	0	0	0	11	75	0	0	10	73	7	100	0	0	4	100	41	65	0	0	7	100	0	0	6	100	0	.	816	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	90	68	29	54	12	73	47	56	18	100	90	38	650	17	133	25	43	42	95	30	25	71	5	100	43	53	0	.	54	47	69	36	108	41	63	39	0	0	84	37	117	24	119	27	189	30	242	24	130	29	151	26	38	62	26	59	152	46	0	0	0	0	0	0	0	0	7	100	44	45	49	52	63	41	76	54	43	55	105	38	51	56	37	48	12	71	34	72	14	58	14	73	44	46	75	40	52	43	76	42	0	0	110	94	10	71	22	64	14	62	45	65	15	73	52	39	57	46	28	54	8	100	41	66	15	60	36	80	39	50	37	50	39	49	55	46	25	62	0	0	38	55	0	0	7	100	5	100	0	0	0	0	46	59	10	100	0	0	0	0	7	100	269	20	122	29	296	21	11	73	0	0	21	62	85	39	27	52	14	71	40	76	6	100	0	.	5268	5
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	37	62	8	100	9	100	106	49	17	71	57	49	574	18	153	32	62	38	44	42	9	100	0	0	32	50	0	.	38	84	50	48	114	34	20	71	0	0	59	40	59	36	85	36	141	25	185	25	145	25	54	38	18	71	50	46	122	41	17	71	0	0	0	0	0	0	0	0	28	73	63	52	75	48	63	73	0	0	87	44	19	76	40	72	17	72	8	100	18	71	0	0	22	58	13	71	69	45	52	48	0	0	64	84	0	0	33	53	85	48	28	59	5	100	17	100	0	0	32	62	0	0	0	0	7	100	7	100	22	58	12	71	30	61	83	38	24	58	9	100	58	47	0	0	0	0	9	100	0	0	15	100	44	60	8	100	10	100	7	100	0	0	242	19	100	31	181	26	12	100	39	62	46	54	55	47	6	100	7	100	11	100	7	100	0	.	4153	6
	broadleaves	7	100	13	73	12	100	19	100	0	0	0	0	22	52	14	71	7	100	14	100	8	100	9	100	29	61	0	.	20	59	0	0	5	100	5	100	0	0	5	100	0	0	0	0	0	0	21	58	4	100	0	0	15	74	8	100	23	100	0	0	0	0	0	0	0	0	0	0	14	75	0	0	15	71	0	0	12	71	20	79	0	0	9	100	0	0	6	100	0	0	8	100	0	0	14	73	0	0	0	0	0	0	11	100	8	100	0	0	0	0	5	72	14	58	3	100	21	86	37	52	6	73	16	58	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	9	100	0	0	7	100	0	0	0	0	0	0	0	0	14	61	0	0	0	0	0	0	11	100	6	100	14	72	0	0	7	100	0	.	557	13
	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	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100
	total	43	55	21	59	21	71	125	44	17	71	57	49	596	18	168	30	69	36	58	40	17	71	9	100	61	43	0	.	58	70	50	48	119	32	26	60	0	0	64	37	59	36	85	36	141	25	206	23	149	25	54	38	33	51	59	42	145	42	17	71	0	0	0	0	0	0	0	0	42	55	63	52	90	44	63	73	12	71	107	39	19	76	49	61	17	72	14	71	18	71	8	100	22	58	27	51	69	45	52	48	0	0	75	73	8	100	33	53	85	48	33	54	19	51	19	87	21	86	69	40	6	73	16	58	7	100	7	100	22	58	12	71	30	61	89	39	24	58	9	100	58	47	0	0	0	0	9	100	0	0	15	100	53	53	8	100	17	71	7	100	0	0	248	19	100	31	195	24	12	100	39	62	46	54	66	42	12	71	21	58	11	100	14	71	0	.	4716	5
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	14	100	29	71	16	100	39	58	0	0	59	80	376	23	204	32	32	71	23	71	0	0	0	0	13	100	0	.	0	0	43	78	165	46	67	41	15	100	79	46	158	36	124	37	142	34	163	31	109	34	39	58	35	58	39	58	63	51	0	0	0	0	0	0	0	0	8	100	39	63	31	59	35	73	46	77	10	100	57	48	29	71	88	57	13	100	34	58	0	0	0	0	0	0	55	60	59	52	23	100	12	100	35	71	0	0	8	100	104	41	18	71	0	0	0	0	0	0	20	71	0	0	28	59	11	100	0	0	67	52	34	58	21	71	38	59	14	100	0	0	34	58	0	0	15	100	0	0	0	0	14	100	14	100	0	0	0	0	11	100	0	0	156	32	52	41	236	28	16	100	0	0	15	100	37	58	14	100	0	0	0	0	12	100	0	.	3612	7
	broadleaves	0	0	11	100	10	100	12	100	0	0	0	0	7	100	8	100	0	0	18	100	32	72	0	0	0	0	0	.	0	0	0	0	0	0	8	100	16	100	0	0	0	0	0	0	0	0	0	0	0	0	23	72	0	0	0	0	26	71	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	11	100	0	0	10	100	0	0	0	0	9	100	0	0	0	0	0	0	7	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	7	100	9	100	7	100	0	0	9	100	0	0	24	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	0	0	0	0	0	.	7	100	0	0	13	100	13	100	10	100	0	0	0	0	0	0	0	0	0	.	308	20
	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	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	total	14	100	40	58	26	73	51	50	0	0	59	80	383	23	212	31	32	71	41	59	32	72	0	0	13	100	0	.	0	0	43	78	165	46	76	38	31	71	79	46	158	36	124	37	142	34	163	31	109	34	62	45	35	58	39	58	89	49	0	0	0	0	0	0	0	0	8	100	39	63	31	59	35	73	46	77	21	71	57	48	39	75	88	57	13	100	44	51	0	0	0	0	0	0	62	54	59	52	23	100	12	100	35	71	0	0	8	100	104	41	25	58	9	100	7	100	0	0	30	58	0	0	52	46	11	100	0	0	67	52	34	58	21	71	38	59	14	100	0	0	34	58	0	0	15	100	0	0	0	0	14	100	14	100	0	0	0	0	11	100	0	0	164	30	52	41	243	28	16	100	13	100	27	71	47	50	14	100	0	0	0	0	12	100	0	.	3928	7
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	0	0	0	0	0	0	0	0	18	100	14	100	254	28	56	58	0	0	0	0	0	.	0	0	0	0	0	.	0	0	14	100	49	79	0	0	0	0	47	59	15	100	44	59	24	71	147	31	39	58	17	100	0	0	25	100	75	50	0	0	0	0	0	0	0	0	0	0	0	0	53	61	26	71	38	71	17	100	52	61	17	100	55	100	0	0	0	0	0	0	0	0	72	100	0	0	58	71	0	0	0	0	0	0	0	0	0	0	0	0	26	72	0	0	13	100	0	.	0	0	13	100	0	0	45	75	12	100	14	100	36	72	0	0	39	100	0	0	20	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	19	100	0	0	68	45	0	0	15	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	17	100	0	.	1563	12
	broadleaves	0	0	0	0	23	100	0	0	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	0	0	0	0	0	0	0	0	0	10	100	0	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	0	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	.	9	100	0	0	13	100	0	0	0	0	0	0	0	0	9	100	0	0	0	.	0	.	0	0	13	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	.	8	100	0	0	0	0	14	100	0	0	0	0	0	0	0	0	0	0	0	.	132	32
	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	23	100	0	0	18	100	14	100	261	28	56	58	0	0	0	0	0	0	0	0	0	0	0	.	0	0	14	100	49	79	0	0	0	0	47	59	15	100	44	59	24	71	157	30	39	58	17	100	0	0	25	100	85	46	0	0	0	0	0	0	0	0	0	0	0	0	53	61	26	71	38	71	17	100	52	61	17	100	55	100	0	0	0	0	0	0	0	0	72	100	0	0	58	71	0	0	0	0	0	0	16	100	0	0	9	100	26	72	13	100	13	100	0	0	0	0	13	100	9	100	45	75	12	100	14	100	36	72	13	100	39	100	0	0	20	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	19	100	0	0	68	45	0	0	22	74	0	0	0	0	14	100	0	0	0	0	0	0	0	0	17	100	0	.	1695	11
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	.	48	72	31	100	21	100	0	0	0	0	0	.	0	0	0	.	0	0	0	0	21	100	18	100	0	.	45	71	0	0	0	0	31	72	16	100	0	0	0	0	26	100	0	0	19	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	27	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	20	100	0	0	0	0	0	0	0	0	9	100	0	0	0	0	20	100	0	.	0	.	16	100	0	0	0	0	0	0	0	0	22	100	45	58	35	71	0	0	23	100	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	47	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	556	19
	broadleaves	0	0	22	100	0	0	0	0	0	.	0	.	0	0	24	100	26	100	0	0	0	.	0	.	0	0	0	.	0	.	15	100	0	0	0	0	0	.	0	.	0	0	0	0	9	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	10	100	0	.	0	0	17	71	0	0	0	.	15	100	0	0	0	0	0	0	0	0	0	0	0	.	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	0	0	0	0	0	.	150	34
	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	22	100	0	0	0	0	0	0	0	.	48	72	55	71	47	71	0	0	0	0	0	.	0	0	0	.	0	0	15	100	21	100	18	100	0	.	45	71	0	0	0	0	41	60	16	100	0	0	0	0	26	100	0	0	19	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	27	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	20	100	0	0	0	0	0	0	0	0	9	100	0	0	10	100	20	100	0	0	17	71	16	100	0	0	15	100	0	0	0	0	22	100	45	58	35	71	0	0	35	100	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	47	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	706	17
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	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	47	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	.	27	100	0	0	0	.	0	.	0	0	0	0	35	100	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	19	100	0	0	0	.	0	.	0	.	18	100	0	0	0	0	0	.	0	0	0	.	0	0	31	100	0	0	34	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	.	210	40
	broadleaves	0	0	23	100	0	.	0	0	0	.	0	.	0	0	0	0	37	100	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	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	10	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	.	95	54
	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	23	100	0	0	0	0	0	0	0	0	0	0	0	0	37	100	0	0	25	100	0	0	0	.	0	.	0	0	0	0	47	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	.	27	100	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	0	.	0	.	0	0	0	0	29	74	0	0	0	0	0	0	0	0	18	100	0	0	0	0	0	.	0	0	0	0	0	0	31	100	0	0	34	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	.	305	32
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	0	0	28	100	0	.	0	.	0	.	0	.	0	0	0	.	0	.	28	100	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	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	38	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	39	71	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	185	39
	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	.	14	100	0	0	13	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	.	26	71
	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	28	100	0	0	0	.	0	.	0	.	0	0	0	.	0	.	28	100	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	26	100	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	13	100	38	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	39	71	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	211	35
>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	.	36	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	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	73
	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
	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	.	36	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	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	73
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	167	41	66	43	50	58	227	39	104	47	271	36	2425	13	727	19	242	23	268	21	37	55	8	78	87	33	3	.	100	41	239	33	609	34	243	31	15	100	424	26	443	21	519	22	671	20	1078	15	694	16	374	20	165	41	163	35	617	29	26	54	11	56	9	52	0	0	30	46	147	34	278	34	255	34	290	58	88	39	384	28	144	40	331	41	60	47	101	42	61	35	26	71	166	47	164	33	366	35	160	37	29	54	224	71	16	69	85	52	277	25	125	36	31	68	70	80	10	77	111	35	62	66	38	47	95	50	59	64	256	34	187	38	174	45	271	31	135	48	45	53	160	34	17	93	28	72	38	40	23	48	42	53	99	52	11	75	18	64	38	82	20	60	1132	14	464	20	1058	15	43	59	44	55	107	42	156	30	66	42	20	55	51	80	42	52	0	.	20111	4
	broadleaves	50	45	107	50	58	79	52	62	11	42	42	39	161	21	94	34	124	39	106	49	69	72	37	41	110	40	0	.	46	43	43	45	31	43	39	36	47	59	72	29	47	30	39	44	76	39	155	24	14	44	161	25	28	49	22	58	133	51	0	0	3	71	2	73	3	100	4	71	32	47	18	61	38	46	7	71	74	43	83	35	54	47	59	37	17	46	22	57	16	45	23	49	30	38	64	64	10	47	42	42	3	72	28	52	38	51	19	47	55	51	51	57	107	36	219	26	326	20	197	24	56	31	266	26	6	60	13	77	3	73	42	42	39	46	48	36	36	52	10	81	22	49	12	58	14	67	5	63	5	69	13	49	61	53	12	89	22	58	0	0	11	65	58	36	35	30	121	28	17	57	13	95	32	60	91	50	31	54	31	53	0	0	20	52	0	.	4765	5
	indeterminable	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	17	96
	total	217	34	173	37	108	50	279	34	115	43	313	33	2586	13	821	18	366	21	373	21	106	51	45	36	197	30	3	.	146	36	282	30	641	33	282	28	62	51	496	23	490	19	559	21	748	18	1233	13	709	15	535	16	193	36	185	34	750	28	26	54	14	49	11	54	3	100	34	42	179	31	296	32	293	31	297	57	162	30	467	24	198	37	391	35	77	39	123	36	77	30	49	49	197	40	228	30	377	34	203	31	32	49	252	64	53	41	104	43	333	23	176	31	138	31	289	31	335	20	308	20	118	43	304	24	101	47	73	61	259	33	229	32	213	38	319	30	171	47	55	46	183	31	29	76	43	53	43	36	28	41	55	42	160	41	23	59	39	43	38	82	31	47	1206	13	499	19	1179	14	60	55	58	48	140	35	247	29	97	33	51	40	51	80	62	46	0	.	24892	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. 2293212. Birmensdorf, Swiss Federal Research Institute WSL

back