Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
growing stock (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 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	90	31	162	41	303	38	129	29	68	40	160	29	946	13	627	13	837	19	410	21	25	57	67	69	60	32	3	.	156	43	221	25	375	24	202	32	29	94	211	28	282	22	291	21	238	16	707	16	770	13	563	17	292	28	319	24	347	17	44	70	133	25	45	37	43	32	165	31	116	37	145	29	154	24	165	28	141	33	162	27	82	38	119	30	103	32	103	36	108	46	107	57	65	33	117	30	208	26	51	40	115	43	129	37	241	48	32	33	141	22	90	31	9	76	1	100	6	100	79	32	92	72	50	38	128	30	62	32	194	35	62	29	31	41	145	27	70	37	39	58	123	29	83	37	34	33	142	23	63	35	99	44	121	34	32	56	71	63	57	67	63	62	724	13	558	15	500	16	130	33	44	50	76	32	177	52	130	44	101	41	44	50	209	48	0	.	16559	3
	broadleaves	254	18	246	26	108	28	200	25	53	44	110	27	459	17	327	17	453	14	481	15	82	42	105	33	181	28	0	.	93	31	245	33	194	28	164	33	120	43	203	32	106	34	87	24	156	26	416	17	180	39	488	16	166	37	80	25	186	22	51	36	67	26	43	41	37	43	51	35	112	45	86	43	184	29	62	33	137	31	155	33	136	30	191	22	307	20	100	29	152	27	117	28	193	24	201	34	94	33	88	38	129	35	67	38	123	30	62	33	84	44	63	37	353	24	384	19	819	15	515	20	382	23	485	20	76	33	89	56	28	43	128	37	102	41	121	23	64	38	50	47	141	27	84	38	46	53	51	32	95	39	95	34	85	27	89	43	50	53	16	45	76	46	164	24	159	21	417	17	76	40	58	44	52	35	143	35	97	38	130	35	61	35	43	35	0	.	14858	3
	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	345	17	408	26	411	30	330	20	121	31	271	23	1405	11	954	11	1290	14	890	14	106	37	172	35	241	24	3	.	248	32	465	22	569	19	366	24	149	42	414	26	388	18	378	19	393	15	1123	12	950	13	1051	12	458	25	398	21	533	14	95	38	200	22	88	32	80	31	215	25	228	30	231	26	338	21	227	23	278	23	317	25	218	25	311	20	410	18	203	26	260	26	223	32	258	21	318	26	302	23	139	32	244	29	196	29	364	34	95	27	225	23	154	24	362	23	385	19	824	15	594	18	474	25	534	18	205	27	151	36	222	32	190	27	133	36	265	22	134	29	89	42	264	22	166	28	80	35	193	22	158	28	194	29	206	28	121	36	122	45	73	57	139	39	888	12	717	13	916	12	206	29	103	34	127	25	320	33	227	30	231	30	106	35	253	42	0	.	31417	2
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	190	33	135	46	313	33	251	27	374	30	535	24	2395	11	1402	13	1629	14	861	17	22	53	52	63	128	29	15	.	168	44	537	26	796	22	461	23	20	84	508	38	877	17	818	18	703	18	1875	12	1780	11	1002	16	411	30	512	21	956	19	125	35	238	31	141	39	97	36	305	27	280	28	371	30	364	24	352	30	180	35	483	21	150	33	489	23	258	32	75	31	189	32	134	44	169	36	246	27	527	24	158	29	371	30	192	34	133	61	38	47	227	26	233	27	38	44	25	63	39	100	197	24	141	48	83	50	228	30	190	44	328	33	109	34	118	34	415	28	109	40	47	81	233	29	155	39	123	40	383	22	84	32	149	32	192	31	26	55	180	47	50	51	250	39	1544	12	1158	14	1235	15	171	36	139	44	195	39	168	31	314	30	169	38	98	44	92	40	0	.	35423	2
	broadleaves	573	19	261	25	230	27	452	27	88	43	301	30	769	19	856	17	1154	15	1163	15	124	43	319	28	426	27	0	.	104	39	252	30	348	29	304	26	162	43	406	26	120	36	139	39	101	39	352	25	90	35	1303	13	310	26	216	35	350	23	77	43	183	36	132	37	87	47	145	33	236	32	20	59	273	30	336	24	364	30	367	23	363	28	593	24	462	21	250	30	285	25	201	29	641	22	153	30	66	46	170	32	279	26	173	34	262	28	54	42	128	26	80	41	383	25	557	18	793	16	416	22	354	24	582	19	112	48	16	58	40	48	173	33	170	39	150	28	292	29	5	71	319	27	89	42	167	39	90	42	219	26	343	26	213	39	162	31	85	39	34	61	208	46	100	33	81	28	431	19	212	43	147	42	89	34	433	27	310	27	161	32	118	38	53	42	0	.	25756	3
	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	763	17	396	23	543	23	703	20	462	26	837	21	3165	9	2258	11	2783	11	2024	12	146	39	371	26	553	23	15	.	272	31	789	20	1143	19	765	19	182	41	914	25	997	16	957	16	804	16	2227	11	1870	11	2305	11	721	22	728	20	1305	16	202	29	421	24	272	29	184	32	450	23	515	24	392	29	637	20	688	21	544	24	850	17	513	24	1082	19	720	19	325	25	474	21	336	26	810	20	398	22	593	22	327	25	650	22	365	26	395	28	92	32	355	20	313	23	421	23	581	18	832	16	613	17	495	23	665	18	340	29	206	43	367	31	282	25	288	30	564	24	401	27	52	73	552	22	244	32	291	30	473	21	302	23	492	22	405	26	188	29	266	36	83	46	457	31	1644	12	1239	13	1667	13	383	30	287	31	284	30	601	24	625	23	330	26	217	30	145	31	0	.	61179	2
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	438	25	267	33	346	32	691	22	593	25	901	23	3859	10	3145	10	2151	12	1763	14	80	45	83	43	294	30	26	.	455	29	638	26	1370	18	808	26	0	.	773	28	1357	18	975	17	1132	16	2747	11	2706	11	1771	16	760	25	1250	17	1384	18	136	41	428	25	345	38	159	36	694	25	369	26	481	23	659	22	843	23	343	46	770	20	308	28	790	20	593	25	226	32	375	29	159	46	412	27	523	25	932	22	229	41	501	28	278	37	217	29	184	43	354	25	394	28	1	519	22	100	32	81	263	23	133	34	265	44	185	31	231	41	489	27	224	38	296	47	582	25	423	39	163	74	336	25	258	46	214	32	563	21	217	32	198	32	382	23	18	81	172	51	94	47	446	41	2154	11	1443	14	1811	13	273	27	321	49	481	33	618	24	640	21	175	40	399	34	294	36	0	.	59280	2
	broadleaves	916	18	397	25	393	29	582	23	114	36	352	28	772	19	822	18	1436	12	1070	18	209	35	456	27	546	22	0	.	208	41	254	36	372	27	472	24	66	56	431	26	137	33	154	36	65	48	223	25	23	56	1931	12	548	32	208	29	324	29	138	35	80	53	139	44	93	36	134	32	341	31	61	58	246	28	330	28	427	31	411	28	413	24	593	24	574	19	362	31	573	25	358	30	734	19	240	39	79	49	265	36	286	30	178	41	509	28	9	75	111	36	78	59	256	26	348	25	497	20	252	26	234	29	344	22	105	41	29	61	19	62	170	45	97	47	203	37	144	37	17	100	269	35	227	32	127	37	42	50	284	27	497	25	260	39	309	37	113	39	19	58	239	36	72	42	27	51	211	33	197	29	168	35	243	27	342	24	298	29	257	34	241	35	117	39	0	.	28516	3
	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	1355	16	664	21	739	23	1272	16	707	24	1253	20	4630	9	3967	10	3587	9	2832	13	288	30	540	26	840	19	26	.	663	25	892	22	1742	16	1281	20	66	56	1204	21	1493	16	1129	16	1196	16	2970	10	2729	11	3702	11	1308	20	1457	16	1709	17	275	28	508	24	484	33	252	30	827	22	711	23	543	23	905	19	1172	20	770	27	1181	17	720	21	1383	17	1167	18	588	25	948	23	517	25	1146	18	764	22	1011	20	495	27	786	22	456	29	725	24	193	41	464	21	472	26	257	26	371	24	529	19	515	18	368	26	609	24	290	27	260	39	507	27	394	30	393	38	785	23	566	32	181	68	605	22	486	32	341	27	606	20	501	24	694	21	642	21	328	36	285	38	113	43	685	30	2226	11	1469	14	2022	12	470	23	489	35	724	26	960	20	937	19	432	27	640	28	410	31	0	.	87796	2
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	394	28	522	31	172	39	1300	20	422	29	1250	21	4844	10	4395	10	3372	11	1664	14	151	48	188	40	269	36	9	.	543	32	929	23	1216	18	946	23	7	100	797	21	1533	15	1235	18	1779	14	3192	10	2916	10	1778	13	873	23	1612	18	1734	18	225	32	628	22	318	37	244	34	761	20	261	35	655	24	742	23	1044	22	340	32	906	21	484	28	1309	19	513	24	546	28	234	33	406	34	344	27	598	26	1077	23	384	26	750	26	457	39	322	30	232	34	399	24	413	24	51	53	35	87	33	52	251	26	182	34	114	39	186	38	218	38	287	28	405	34	298	29	683	22	435	29	249	45	351	25	150	32	296	33	569	21	313	29	429	34	518	25	129	59	217	45	134	52	483	36	2059	11	1535	14	1666	12	463	32	408	36	707	24	717	24	631	24	348	33	399	31	418	34	0	.	70996	2
	broadleaves	783	17	462	24	202	32	435	27	62	40	317	32	850	21	698	22	1564	13	687	19	273	33	239	35	540	28	0	.	186	40	160	35	221	30	374	30	79	59	289	25	134	53	136	39	31	48	110	38	41	71	1428	15	597	34	175	32	328	34	158	37	24	78	132	50	22	75	94	36	202	41	30	61	329	39	284	31	260	34	304	28	337	32	589	31	339	29	470	25	342	34	375	29	523	24	335	28	49	85	145	34	200	36	247	36	242	25	11	71	97	33	34	78	138	38	258	33	184	26	181	24	122	39	240	25	44	98	19	78	13	72	91	39	154	48	147	37	253	34	22	100	218	33	226	43	50	47	37	46	128	44	285	31	221	40	156	45	139	38	31	71	203	41	24	63	6	100	116	41	163	36	245	34	229	29	358	27	354	29	296	29	164	35	221	36	0	.	23306	3
	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	1177	16	984	21	373	25	1734	17	484	27	1567	19	5693	9	5093	9	4937	9	2351	12	424	30	426	28	809	23	9	.	729	28	1089	21	1437	16	1320	20	86	55	1086	18	1667	15	1371	17	1810	14	3302	10	2956	9	3205	10	1469	20	1787	17	2062	16	383	25	652	22	450	31	266	32	855	19	463	28	685	23	1071	21	1328	19	600	25	1210	18	822	23	1898	17	851	21	1016	20	576	26	781	25	867	21	933	22	1126	22	529	22	950	23	703	32	564	24	243	33	495	21	446	23	189	32	293	31	216	24	431	19	304	26	354	23	230	36	238	37	300	28	496	29	452	28	830	21	688	25	271	43	569	22	376	30	346	31	606	20	441	26	714	25	739	22	286	38	356	36	165	45	685	29	2083	11	1540	14	1782	12	626	27	653	27	936	21	1074	21	985	20	644	25	563	25	639	28	0	.	94303	2
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	308	36	346	36	246	32	936	21	260	40	849	22	4003	11	3732	11	3361	12	1149	20	137	72	65	48	198	36	0	.	413	33	419	29	900	23	560	26	12	100	480	24	1366	17	1141	17	1373	15	2521	11	1990	12	1468	14	810	27	1133	19	1359	18	149	39	383	26	163	48	191	43	973	21	224	40	386	25	518	27	670	27	262	37	891	24	462	27	1384	19	346	24	430	35	106	43	250	41	140	44	492	27	615	25	336	37	563	28	243	41	293	34	255	34	472	21	284	27	21	71	72	53	24	100	257	25	105	40	205	43	202	50	178	38	304	31	352	34	158	39	608	26	269	37	95	58	432	33	112	45	270	36	307	23	331	35	373	33	326	30	76	67	210	40	91	50	277	47	1729	14	970	19	1325	16	421	38	234	36	439	32	437	28	545	27	250	36	363	44	387	36	0	.	55776	2
	broadleaves	715	22	295	30	209	30	308	31	22	98	158	42	747	30	370	24	898	17	233	28	355	30	223	42	379	31	0	.	166	47	77	54	201	40	266	33	53	100	224	38	118	45	54	45	8	100	113	47	12	72	611	20	358	36	138	47	153	38	88	59	20	74	82	87	0	.	12	78	110	46	9	100	304	49	152	40	85	47	112	46	90	50	398	29	260	29	422	29	29	78	118	40	206	32	181	53	64	100	122	46	143	43	127	49	233	32	0	.	67	40	13	71	68	54	144	38	102	40	158	40	39	64	134	33	68	62	26	100	0	.	114	43	83	47	9	100	153	53	0	.	60	73	151	43	0	.	12	100	73	75	113	42	81	41	12	109	83	46	48	61	209	38	15	71	0	.	93	43	79	49	182	47	339	32	220	42	349	36	64	52	105	53	122	38	0	.	14425	4
	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	1024	20	640	25	455	24	1244	19	282	39	1007	21	4751	10	4102	11	4259	10	1382	17	491	30	288	37	577	25	0	.	579	28	496	26	1101	20	826	22	65	84	704	21	1484	17	1195	16	1381	15	2634	10	2001	12	2079	12	1169	22	1271	18	1512	17	238	33	404	27	245	43	191	43	986	21	335	31	395	24	821	26	822	24	347	30	1004	22	552	24	1782	17	606	20	852	26	134	38	369	31	346	27	674	25	679	24	458	31	707	26	370	33	527	25	255	34	539	19	298	26	89	44	216	31	126	38	415	22	144	34	340	29	270	43	204	36	304	31	467	30	242	31	618	26	422	31	95	58	492	30	263	34	270	36	319	24	404	32	486	29	407	27	89	60	293	36	139	47	486	31	1744	14	970	19	1419	15	500	33	417	31	778	23	657	25	894	24	314	32	468	36	509	31	0	.	70201	2
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	113	41	116	50	108	53	367	31	105	60	201	34	1719	15	2121	14	1563	15	444	26	47	71	98	69	57	71	0	.	158	39	212	36	433	29	194	36	12	100	425	31	700	20	586	24	785	20	1316	16	534	22	601	24	501	33	497	24	426	30	85	69	148	37	53	59	127	49	347	26	29	58	278	32	299	33	236	36	265	50	283	33	278	33	749	28	154	43	292	38	59	58	120	43	29	100	313	37	371	33	395	32	152	59	110	55	257	40	248	37	356	33	141	43	25	71	0	.	0	.	70	47	85	46	80	47	126	57	158	66	94	60	142	40	189	36	256	32	228	37	67	72	196	37	113	54	227	43	16	100	196	43	75	50	84	45	0	.	104	70	62	58	100	74	1034	19	409	25	653	21	234	38	38	71	140	45	224	42	136	50	87	50	70	50	134	50	0	.	26469	3
	broadleaves	298	37	109	46	61	58	200	47	0	.	19	100	187	41	122	55	504	30	49	58	197	47	22	100	110	71	0	.	0	.	64	52	89	62	88	78	0	.	0	.	58	71	17	100	7	100	36	54	0	.	359	30	0	.	59	100	67	71	15	100	0	.	0	.	38	71	0	.	0	.	0	.	13	100	101	45	38	80	46	75	62	71	18	100	148	39	84	59	19	100	38	71	77	82	12	100	0	.	130	52	112	52	41	71	80	52	0	.	17	71	14	78	28	58	64	62	26	72	89	37	11	100	62	42	0	.	0	.	15	100	63	59	12	100	0	.	150	61	0	.	0	.	92	66	18	100	0	.	0	.	19	100	0	.	0	.	62	72	0	.	14	100	0	.	0	.	12	100	17	100	108	67	111	45	57	75	86	63	78	50	11	100	41	71	0	.	5071	8
	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	411	30	225	34	169	40	567	26	105	60	220	35	1906	14	2243	13	2067	14	492	25	244	40	121	59	167	53	0	.	158	39	276	31	522	28	282	35	12	100	425	31	759	19	603	23	792	19	1352	16	534	22	961	20	501	33	556	24	493	27	100	60	148	37	53	59	165	41	347	26	29	58	278	32	312	32	337	28	303	46	329	31	339	32	767	28	301	29	376	34	77	50	158	37	106	66	326	36	371	33	525	31	264	41	151	48	338	33	248	37	373	31	156	40	53	45	64	62	26	72	159	29	95	42	142	32	126	57	158	66	109	53	205	33	201	34	256	32	377	39	67	72	196	37	206	42	245	41	16	100	196	43	94	45	84	45	0	.	166	52	62	58	114	66	1034	19	409	25	665	21	250	36	146	53	251	32	281	38	222	39	165	35	81	45	174	42	0	.	31540	3
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	23	100	76	75	20	100	143	50	74	76	0	.	460	24	769	24	658	24	288	33	26	100	0	.	29	100	0	.	92	100	23	100	116	41	102	58	0	.	212	35	297	32	353	38	343	28	769	22	278	27	156	38	29	100	32	100	89	58	68	58	101	59	17	100	87	63	123	80	31	100	122	61	81	60	22	100	73	76	126	42	185	40	501	33	0	.	144	44	24	100	25	100	0	.	134	43	103	58	168	55	61	58	0	.	46	71	49	59	139	40	143	49	0	.	0	.	0	.	83	45	54	72	57	73	38	71	67	73	76	65	74	59	121	46	71	58	107	50	0	.	19	100	45	71	28	100	53	71	14	100	121	54	38	71	0	.	70	58	63	100	16	100	662	20	154	38	256	33	24	100	0	.	41	72	49	71	53	71	0	.	62	72	25	100	0	.	10574	6
	broadleaves	71	58	65	84	22	100	25	100	0	.	0	.	52	75	88	60	340	36	0	.	97	77	0	.	51	71	0	.	0	.	25	100	41	72	33	100	0	.	11	100	0	.	0	.	0	.	21	77	0	.	74	53	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	27	100	13	100	32	71	45	100	0	.	0	.	88	77	20	100	0	.	0	.	0	.	0	.	29	100	19	100	33	100	100	63	0	.	68	47	10	100	14	100	43	79	52	51	36	59	38	76	70	47	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	15	100	0	.	74	72	61	100	35	75	35	100	0	.	43	71	0	.	0	.	2220	12
	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	94	61	141	56	42	71	168	45	74	76	0	.	512	23	856	22	999	20	288	33	122	64	0	.	80	58	0	.	92	100	48	100	157	36	135	50	0	.	223	33	297	32	353	38	343	28	790	21	278	27	230	31	29	100	65	71	89	58	68	58	101	59	17	100	87	63	123	80	41	80	122	61	81	60	49	71	86	66	158	36	229	45	501	33	0	.	232	51	44	71	25	100	0	.	134	43	103	58	196	49	80	50	33	100	146	48	49	59	208	31	153	46	14	100	43	79	52	51	119	36	92	52	128	41	53	58	67	73	76	65	74	59	121	46	71	58	107	50	0	.	19	100	167	48	28	100	53	71	14	100	121	54	38	71	0	.	70	58	63	100	42	73	662	20	154	38	270	32	24	100	74	72	102	66	83	52	89	58	0	.	105	61	25	100	0	.	12795	5
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	.	106	59	0	.	43	100	252	39	668	27	255	36	63	71	0	.	35	100	0	.	0	.	35	100	61	71	0	.	0	.	0	.	84	51	27	100	87	53	105	46	110	50	214	40	92	58	23	100	0	.	0	.	0	.	0	.	32	100	0	.	30	100	0	.	0	0	24	100	0	.	56	71	87	52	0	.	178	47	0	.	0	.	0	.	30	100	0	.	29	100	29	100	0	.	0	.	0	.	0	.	30	71	72	71	44	71	0	.	0	.	0	.	100	54	0	0	0	.	0	.	0	.	33	100	102	65	78	72	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	32	99	52	82	52	0	.	0	.	0	.	31	100	0	.	85	76	0	.	0	.	0	.	3752	9
	broadleaves	31	100	19	100	27	100	0	.	0	.	0	.	63	58	35	100	83	72	0	.	38	100	0	.	32	100	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	12	100	35	71	0	.	0	.	59	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	58	100	0	.	0	.	28	100	0	.	0	.	62	74	32	100	56	100	0	.	0	.	115	52	15	100	0	.	0	.	28	71	26	71	0	.	35	72	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	21	100	0	.	0	.	40	100	0	.	45	100	24	100	26	100	35	100	0	.	1239	17
	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	100	19	100	27	100	106	59	0	.	43	100	314	33	702	26	338	32	63	71	38	100	35	100	32	100	0	.	35	100	61	71	0	.	67	100	0	.	84	51	27	100	87	53	118	42	145	42	214	40	92	58	82	77	0	.	0	.	0	.	0	.	32	100	0	.	30	100	0	.	0	0	24	100	0	.	56	71	87	52	0	.	193	46	0	.	58	100	0	.	30	100	28	100	29	100	29	100	62	74	32	100	56	100	0	.	30	71	186	42	59	59	0	.	0	.	28	71	126	45	0	0	35	72	0	.	0	.	57	71	102	65	78	72	18	100	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	29	100	0	.	0	.	226	32	99	52	103	46	0	.	0	.	40	100	31	100	45	100	110	63	26	100	35	100	0	.	4990	8
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	.	29	100	0	.	0	.	0	.	0	.	18	100	81	71	36	100	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	74	0	.	110	52	0	.	44	100	33	100	32	100	27	71	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	44	100	0	.	66	72	0	0	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	71	0	.	0	.	46	100	58	71	93	71	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	20	100	31	100	0	.	0	.	0	.	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	51	21	100	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1221	17
	broadleaves	0	.	34	100	0	.	41	100	0	.	0	.	0	.	0	.	88	71	0	.	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	17	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	.	23	100	97	67	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	422	30
	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	.	62	71	0	.	41	100	0	.	0	.	18	100	81	71	124	58	31	100	49	100	0	.	0	.	0	.	0	.	0	.	0	.	63	74	0	.	110	52	0	.	44	100	33	100	65	71	45	58	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	44	100	0	.	66	72	0	0	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	71	0	.	0	.	46	100	58	71	93	71	0	.	0	.	23	100	97	67	23	100	0	.	6	100	0	.	20	100	31	100	0	.	0	.	0	.	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	51	21	100	26	100	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1643	15
>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	.	118	59	47	100	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	29	100	0	.	33	100	161	47	64	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	121	59	0	.	121	72	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	73	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	940	21
	broadleaves	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	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	.	47	72	0	.	232	100	139	100	43	100	325	59	0	.	105	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	982	35
	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	.	35	100	0	.	0	.	0	.	0	.	118	59	47	100	42	100	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	79	29	100	0	.	33	100	161	47	64	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	37	100	47	72	0	.	232	100	139	100	43	100	446	46	0	.	225	47	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	73	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1922	21
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	1557	22	1652	26	1508	24	3922	17	1897	23	3939	18	18612	8	16988	8	13904	9	6671	12	487	45	588	33	1035	28	53	.	2020	25	3040	18	5206	15	3337	18	80	62	3654	19	6469	12	5530	13	6524	12	13429	8	11280	8	7464	11	3699	19	5355	15	6295	14	833	32	2058	19	1115	30	949	29	3397	18	1310	24	2439	20	2864	20	3375	20	1660	28	3775	15	1949	22	5556	16	1967	19	1816	23	1094	24	1232	30	1158	22	2452	19	3860	18	1807	23	2513	22	1409	28	1554	22	1163	24	2252	17	1742	21	145	44	154	57	133	61	1445	18	792	27	975	31	1094	27	1125	32	1836	24	1469	25	1314	26	2778	19	1683	26	660	45	1691	21	916	29	1192	27	2034	16	1218	26	1443	23	1660	20	282	49	1024	35	550	42	1634	31	10269	9	6365	11	7555	10	1716	26	1185	30	2079	23	2421	19	2449	19	1215	25	1436	28	1559	26	0	.	280990	1
	broadleaves	3642	14	1922	18	1252	22	2243	19	338	31	1258	25	3899	16	3317	14	6521	10	3682	12	1423	26	1405	24	2264	19	0	.	756	35	1076	25	1465	23	1767	22	480	37	1582	20	673	30	587	26	379	28	1340	16	361	32	6194	10	2039	24	909	26	1409	20	528	28	375	30	528	34	277	35	436	23	1011	25	207	43	1349	28	1293	23	1324	27	1427	22	1445	23	2397	20	2089	15	1834	21	1419	19	1208	23	2402	17	1123	25	353	42	1010	26	1199	22	921	28	1549	20	136	33	733	26	308	45	1472	24	1960	18	2640	13	1996	18	1180	19	2063	16	421	35	179	52	138	38	740	27	617	34	630	25	1055	28	95	49	1006	23	991	29	409	29	232	28	823	25	1352	23	860	27	729	30	562	35	148	45	975	31	376	26	272	21	1316	16	743	27	1016	28	1164	26	1586	21	1575	22	1010	25	770	28	632	30	0	.	116795	2
	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	5199	13	3574	17	2760	18	6165	14	2235	22	5197	17	22512	8	20305	8	20425	7	10353	10	1910	24	1993	22	3298	18	53	.	2776	22	4116	17	6672	14	5104	16	560	36	5236	15	7141	11	6117	12	6903	12	14768	8	11641	8	13658	8	5738	16	6264	14	7704	13	1361	24	2433	18	1642	26	1226	26	3833	17	2321	20	2646	19	4214	17	4668	17	2984	21	5202	14	3393	19	7953	14	4055	15	3649	18	2513	18	2440	21	3560	16	3575	18	4213	17	2817	20	3713	18	2330	23	3103	18	1299	22	2985	15	2050	19	1617	22	2115	17	2773	13	3441	13	1972	17	3038	16	1514	24	1303	30	1975	23	2209	20	1931	22	3408	18	2738	23	755	41	2697	18	1907	24	1601	24	2266	16	2041	21	2795	18	2520	17	1011	28	1586	29	697	40	2609	24	10645	9	6637	11	8871	9	2459	22	2202	22	3242	19	4008	17	4025	17	2225	21	2205	23	2191	23	0	.	397785	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2298357. Birmensdorf, Swiss Federal Research Institute WSL

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