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

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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	98	39	182	55	142	82	79	51	64	64	162	42	766	18	713	18	758	23	405	29	4	100	96	86	45	47	5	.	168	52	266	29	321	39	239	42	52	94	172	38	287	29	284	31	160	27	491	18	743	19	571	26	273	36	415	29	426	22	66	83	148	36	54	48	45	45	207	41	116	57	174	35	127	37	113	45	138	46	88	52	71	41	130	32	149	40	78	46	93	68	96	79	56	46	77	32	241	38	64	37	105	69	202	41	393	52	24	55	143	28	108	35	3	79	0	.	0	.	96	42	23	48	27	45	136	44	63	47	260	42	68	41	19	49	102	48	73	56	61	65	151	39	83	52	18	55	152	33	61	54	130	59	97	51	41	75	65	80	4	100	23	59	774	20	545	20	438	25	140	46	36	72	69	43	256	61	174	55	87	65	45	73	82	44	0	.	16095	4
	broadleaves	204	24	233	37	97	42	165	34	22	54	96	40	447	28	386	19	504	19	582	20	87	59	119	36	192	40	0	.	86	41	287	41	266	34	96	32	166	52	189	35	127	47	103	32	142	39	353	25	118	47	454	26	87	33	106	30	181	24	52	51	92	31	50	54	56	50	40	70	108	59	79	69	169	35	72	39	154	39	167	32	159	41	182	32	327	27	80	41	134	35	146	34	127	30	134	53	69	45	103	52	78	39	41	72	153	39	63	48	40	138	49	52	409	32	466	25	810	20	420	36	326	29	444	26	111	38	42	91	30	49	112	41	74	77	89	38	35	45	44	62	133	34	19	82	23	45	75	36	137	46	112	49	62	39	133	50	55	74	13	72	67	56	139	36	124	32	326	25	36	61	48	49	45	54	158	42	73	45	149	49	64	40	48	49	0	.	14267	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	302	24	415	39	239	54	244	29	86	53	258	34	1213	16	1099	15	1262	17	986	18	91	57	214	44	237	34	5	.	254	43	553	28	587	27	335	32	217	49	361	27	414	25	387	28	302	24	844	15	861	17	1024	19	360	29	521	26	608	18	118	52	240	28	105	40	101	42	247	37	224	41	254	33	295	27	185	33	291	31	255	34	230	33	312	26	476	23	158	36	227	36	242	40	184	29	211	40	309	34	167	40	183	45	244	38	546	39	86	41	183	39	157	30	413	32	466	25	810	20	515	31	350	28	471	24	247	37	105	46	290	39	180	30	93	65	191	41	108	45	105	56	284	29	102	50	41	36	228	30	198	37	242	39	159	41	174	43	119	59	17	71	90	48	913	18	669	17	764	18	176	39	83	41	115	36	414	42	247	41	236	43	110	49	130	43	0	.	30362	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	203	48	137	70	106	58	240	32	386	35	584	32	2167	13	1557	17	1701	18	1124	21	0	.	61	87	132	42	26	.	143	62	711	31	711	30	568	30	36	84	511	54	774	19	593	26	629	23	1784	17	1900	16	1077	23	340	39	642	26	1030	26	151	44	290	42	147	39	129	44	313	34	318	37	320	42	389	33	290	36	123	45	434	31	110	45	392	37	329	38	77	46	203	45	86	63	72	35	131	38	554	34	186	35	429	41	261	40	191	75	29	60	214	40	277	33	42	62	0	.	0	.	183	33	116	51	56	91	161	36	224	57	420	41	69	43	146	37	245	59	76	63	73	91	264	35	139	56	91	63	428	28	73	40	159	42	154	42	29	80	219	59	43	60	183	64	1212	16	1211	18	1073	19	101	57	57	51	224	44	191	35	415	36	138	42	106	61	98	49	0	.	34735	3
	broadleaves	336	33	292	33	123	49	419	32	44	54	349	33	778	26	884	21	1309	18	1182	18	153	52	323	36	350	33	0	.	87	60	233	36	449	37	282	36	219	53	421	34	142	48	103	35	126	54	399	35	72	54	961	20	244	34	274	44	392	30	47	61	201	40	147	45	106	67	102	40	236	44	0	.	291	41	315	32	462	38	352	29	303	47	721	32	459	29	215	46	283	34	202	40	608	30	120	48	64	48	181	40	263	35	154	43	244	29	33	63	109	34	75	58	254	32	644	23	741	22	352	33	377	33	571	29	180	49	0	.	30	78	154	50	158	61	130	44	256	44	5	100	374	34	32	61	81	52	69	51	293	31	369	36	171	53	205	38	121	46	47	73	32	77	87	43	55	40	407	25	93	58	131	52	69	53	579	30	303	29	95	39	176	43	55	50	0	.	24936	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	539	28	429	32	229	41	659	25	430	33	933	28	2945	12	2441	14	3009	13	2306	16	153	52	384	34	482	31	26	.	230	45	945	25	1160	25	850	25	255	49	931	34	916	18	697	23	754	21	2183	15	1971	16	2038	15	584	29	916	24	1422	22	198	39	492	30	294	33	234	42	415	31	555	31	320	42	680	27	604	28	586	31	786	23	412	37	1114	27	787	25	292	37	486	30	288	34	680	29	251	34	618	31	368	31	693	30	416	33	435	37	61	43	323	30	352	28	296	29	644	23	741	22	535	25	494	29	627	28	341	36	224	57	451	39	222	37	304	42	375	45	332	43	78	86	637	29	170	53	172	48	498	26	366	29	527	31	325	36	234	36	339	44	91	61	215	56	1298	15	1266	17	1480	16	193	42	188	42	292	37	770	28	718	26	233	35	282	37	153	40	0	.	59671	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	351	39	216	45	286	41	832	26	640	31	1017	29	3515	13	3049	14	2564	15	1754	18	43	77	77	65	279	42	46	.	363	46	817	33	1267	27	1083	32	0	.	804	39	1359	23	707	24	1127	23	2505	16	2904	14	1686	23	815	29	1643	20	1312	23	162	54	444	36	221	43	195	47	685	36	334	39	356	34	784	30	620	31	194	44	397	29	217	47	634	30	670	30	115	41	401	40	221	56	308	37	360	34	800	27	360	42	263	36	197	37	197	32	159	43	405	30	527	36	9	100	0	.	13	100	213	37	128	49	223	60	216	40	252	61	648	33	135	72	430	53	192	52	260	45	221	95	367	33	266	62	180	47	405	29	167	43	221	41	376	29	15	132	155	51	97	62	447	63	1788	17	1379	18	1548	19	172	48	130	52	609	37	711	29	749	26	288	43	399	50	279	45	0	.	56972	3
	broadleaves	832	24	360	36	265	48	613	29	80	58	262	36	809	25	809	23	1488	15	1189	23	209	43	406	40	415	32	0	.	152	56	244	46	333	36	453	33	51	87	488	33	168	41	164	43	78	67	205	37	23	79	1875	17	682	42	271	35	421	37	153	52	24	188	147	61	107	42	125	51	275	44	33	83	358	32	348	33	575	38	334	31	310	34	682	33	703	24	204	53	469	36	387	39	640	28	188	58	59	63	378	43	277	35	174	49	490	32	5	100	62	70	19	73	142	38	337	32	546	27	172	37	152	35	421	29	137	49	19	100	14	100	224	52	114	63	164	60	117	53	31	100	370	35	172	51	129	41	32	83	359	33	444	34	297	52	339	50	91	55	34	58	87	58	86	54	13	100	267	40	137	47	175	44	187	40	342	32	217	31	253	39	232	47	156	43	0	.	27877	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	1183	23	577	30	551	36	1444	20	720	30	1279	27	4324	12	3858	13	4053	12	2942	16	251	43	483	40	694	28	46	.	516	38	1061	29	1600	23	1536	26	51	87	1292	29	1528	21	870	23	1205	22	2710	15	2927	14	3561	15	1497	26	1914	19	1733	21	315	39	467	37	368	38	302	40	809	32	608	34	389	34	1142	24	969	26	769	31	731	23	526	30	1316	25	1373	22	320	41	869	32	607	32	948	26	548	31	859	26	739	30	540	30	371	33	687	28	164	42	467	27	546	35	151	36	337	32	559	27	385	26	280	34	644	31	352	32	271	57	662	32	359	44	544	44	356	39	377	40	251	85	737	26	438	51	309	40	437	27	527	31	665	29	673	29	355	49	245	48	131	54	535	54	1874	17	1392	18	1815	17	309	36	305	36	796	33	1053	25	965	23	541	31	632	39	435	39	0	.	84848	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	322	47	607	43	137	57	1649	25	606	33	1368	26	4965	13	3791	13	3405	14	1737	18	99	84	18	108	346	47	16	.	294	50	1127	28	1167	24	1318	28	12	100	826	26	1527	21	925	22	1907	19	3051	15	2714	13	1673	17	757	28	1898	22	1878	22	245	43	523	29	349	41	213	44	795	25	249	50	500	38	935	32	622	37	266	47	701	33	519	39	1134	28	575	27	587	39	257	43	591	40	344	33	330	31	1301	30	396	35	684	40	434	38	355	32	225	41	415	31	567	29	57	70	0	.	50	59	140	48	234	43	135	50	191	49	296	48	368	36	482	44	268	46	307	33	331	47	121	78	284	33	86	53	186	53	406	26	215	42	379	58	559	33	193	69	165	48	80	70	451	52	1688	17	1591	19	1562	17	271	51	337	49	672	32	896	30	690	30	487	37	329	44	412	44	0	.	69170	3
	broadleaves	831	24	427	32	116	52	568	32	73	52	215	43	759	27	1047	25	1964	16	581	25	244	47	199	44	259	40	0	.	107	72	121	49	174	38	297	38	58	100	365	30	213	55	155	40	47	55	142	50	53	85	1323	20	657	50	156	45	317	42	135	54	33	100	80	54	39	76	88	51	110	62	31	73	366	52	250	51	372	40	289	34	166	46	644	44	397	36	430	35	406	45	468	37	409	34	257	38	71	100	173	42	233	46	395	37	252	31	0	.	77	45	48	100	97	54	139	40	216	35	165	37	52	90	237	36	53	129	0	.	23	72	98	46	159	61	75	62	156	56	39	100	283	40	154	65	77	52	42	59	157	56	190	42	230	58	182	60	169	47	61	65	75	71	19	124	0	.	108	55	160	51	356	38	169	46	361	34	272	36	337	38	224	41	311	41	0	.	23128	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	1153	23	1035	30	253	40	2217	22	679	32	1583	25	5725	13	4838	12	5369	11	2317	16	343	50	217	45	605	33	16	.	401	44	1248	26	1341	22	1615	25	70	85	1191	22	1740	19	1080	21	1955	19	3193	14	2767	13	2996	14	1414	28	2055	21	2195	20	380	33	555	30	428	36	252	40	882	25	359	39	531	37	1301	28	872	31	638	32	990	26	685	32	1778	25	971	25	1017	29	663	34	1059	32	752	30	588	27	1371	29	569	30	917	34	829	34	607	25	225	41	493	28	614	28	154	46	139	40	266	32	305	30	287	40	372	30	243	46	296	48	391	35	580	37	427	39	381	31	487	41	160	68	567	29	240	49	263	49	447	24	372	38	569	43	789	30	375	49	334	41	141	51	526	47	1707	17	1591	19	1670	16	431	39	693	33	841	29	1258	27	962	27	823	31	553	33	722	34	0	.	92298	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	330	52	575	39	155	49	863	26	396	44	699	30	3882	13	3729	15	3417	16	984	22	184	90	21	100	207	50	0	.	225	52	378	43	1020	29	578	32	21	100	597	29	1195	26	1019	23	1289	20	2191	15	2093	17	1245	20	842	36	1263	23	1634	23	185	51	284	38	267	52	233	59	769	30	289	48	296	37	554	35	533	38	314	49	669	41	552	36	995	28	503	27	315	58	146	53	343	50	84	62	447	35	593	31	333	43	289	48	270	49	378	41	299	44	420	28	318	35	40	70	43	75	46	100	190	44	99	51	218	53	262	59	194	48	289	43	307	48	153	55	349	47	172	54	0	.	501	45	11	100	289	51	326	30	297	50	224	47	422	39	111	80	182	54	95	76	254	67	1179	21	902	22	1409	20	211	44	311	42	452	40	486	33	387	34	350	41	384	69	266	49	0	.	53122	3
	broadleaves	730	30	324	39	120	50	316	43	18	174	82	71	562	35	490	30	981	22	193	38	301	42	63	60	257	47	0	.	49	100	83	64	62	80	177	47	94	100	165	63	148	56	77	50	0	.	101	75	20	72	743	24	353	42	141	58	100	62	42	75	24	100	-3	100	0	.	6	100	65	74	0	.	398	57	97	64	76	71	168	50	26	140	412	41	267	36	430	41	50	78	81	58	204	39	80	58	109	100	129	56	173	51	177	55	227	43	0	.	33	71	13	100	57	60	84	53	63	67	93	80	41	100	156	43	112	62	0	.	0	.	145	51	103	60	0	.	142	84	0	.	106	73	49	72	0	.	21	100	133	76	67	72	55	71	24	100	63	72	44	71	113	53	0	.	0	.	62	64	40	71	140	52	139	48	315	47	410	49	108	51	130	64	117	45	0	.	13161	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1060	28	899	30	275	40	1178	23	413	44	781	30	4444	12	4219	14	4398	14	1177	20	485	43	84	51	464	34	0	.	274	46	461	37	1083	28	755	28	115	84	762	27	1342	24	1096	22	1289	20	2292	15	2113	17	1988	16	1195	29	1404	22	1734	23	227	44	308	41	263	52	233	59	775	30	354	41	296	37	952	33	630	34	389	42	837	34	578	34	1407	23	770	23	745	40	196	44	424	43	287	37	527	32	702	30	463	37	462	37	447	39	604	31	299	44	452	27	331	33	96	45	128	43	109	57	284	39	139	46	373	36	375	48	194	48	289	43	452	40	255	41	349	47	314	50	0	.	606	39	59	62	289	51	347	30	430	42	292	48	476	37	135	68	245	47	139	67	367	49	1179	21	902	22	1470	20	251	41	451	35	591	34	801	29	797	32	458	35	514	54	383	41	0	.	66283	3
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	80	71	189	55	96	76	334	44	181	60	96	59	1360	19	2004	17	1499	20	263	41	45	100	112	100	104	72	0	.	75	73	214	50	681	33	73	61	0	.	537	40	764	25	366	36	663	28	1168	21	437	27	483	36	483	49	640	26	481	41	55	71	116	50	95	58	119	58	302	36	15	100	246	43	332	47	128	60	293	66	334	42	450	34	634	39	175	58	341	43	37	100	135	60	52	100	379	43	207	45	319	49	85	72	184	55	408	42	295	49	272	54	156	61	48	71	0	.	0	.	106	53	81	59	70	75	103	50	130	85	60	100	171	47	109	62	46	72	204	52	0	.	154	56	35	100	128	78	29	100	142	64	37	100	89	58	0	.	155	83	45	100	57	100	798	30	290	32	608	26	66	71	65	71	211	50	182	45	33	100	72	71	63	71	165	65	0	.	24138	5
	broadleaves	321	50	98	58	75	71	272	54	0	.	0	.	182	48	214	55	403	44	29	100	194	58	40	100	32	100	0	.	0	.	74	59	60	76	43	100	0	.	0	.	96	71	27	100	0	.	38	71	0	.	353	33	0	.	0	.	49	100	0	.	0	.	0	.	33	100	0	.	0	.	0	.	23	100	80	71	64	79	79	74	49	100	0	.	81	73	42	100	33	100	39	100	0	.	0	.	0	.	81	100	96	74	41	100	42	100	0	.	15	100	20	100	52	58	83	78	19	100	66	51	20	100	58	59	0	.	0	.	28	100	107	58	22	100	0	.	145	75	0	.	0	.	34	100	32	100	0	.	0	.	35	100	0	.	0	.	42	100	0	.	25	100	0	.	0	.	0	.	0	.	0	.	37	100	94	75	148	62	103	58	19	100	36	100	0	.	4622	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	400	43	287	41	172	53	606	34	181	60	96	59	1542	18	2219	17	1902	19	292	38	239	50	152	78	136	59	0	.	75	73	288	42	741	34	115	53	0	.	537	40	860	23	393	34	663	28	1206	20	437	27	836	27	483	49	640	26	530	38	55	71	116	50	95	58	151	50	302	36	15	100	246	43	354	44	208	46	356	58	413	38	499	36	634	39	255	46	383	44	70	70	174	52	52	100	379	43	207	45	400	48	180	52	225	53	449	39	295	49	286	51	176	55	100	45	83	78	19	100	172	38	101	51	129	49	103	50	130	85	88	75	278	36	131	55	46	72	349	44	0	.	154	56	69	70	160	65	29	100	142	64	72	71	89	58	0	.	196	69	45	100	82	76	798	30	290	32	608	26	66	71	65	71	248	45	277	42	182	54	175	45	82	59	202	56	0	.	28760	4
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	40	100	129	74	33	100	191	59	127	76	0	.	373	32	660	28	669	33	212	52	46	100	0	.	0	.	0	.	0	.	44	100	122	50	66	71	0	.	325	37	300	39	294	50	344	33	905	28	321	34	163	50	0	.	58	100	124	71	77	71	109	72	32	100	121	75	52	100	0	.	0	.	29	100	40	100	122	76	60	73	284	43	334	54	0	.	69	71	43	100	45	100	0	.	122	58	56	100	110	60	111	58	0	.	81	71	37	100	136	61	145	64	0	.	0	.	0	.	66	73	0	.	42	100	64	71	42	100	59	71	28	100	76	71	0	.	102	71	0	.	33	100	40	100	0	.	95	71	0	.	0	.	0	.	0	.	90	72	115	100	27	100	695	28	164	49	189	49	42	100	0	.	28	100	83	71	50	100	0	.	0	.	0	.	0	.	9891	7
	broadleaves	88	71	88	100	36	100	46	100	0	.	0	.	58	100	156	60	320	49	0	.	123	100	0	.	50	100	0	.	0	.	46	100	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	71	73	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	34	100	76	100	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	0	.	35	100	54	100	177	63	0	.	30	100	18	100	26	100	20	100	55	72	39	71	0	.	95	52	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	130	72	0	.	18	100	58	100	0	.	36	100	0	.	0	.	2237	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	128	72	217	60	69	71	237	51	127	76	0	.	431	31	816	25	989	28	212	52	168	77	0	.	50	100	0	.	0	.	90	100	122	50	66	71	0	.	344	35	300	39	294	50	344	33	905	28	321	34	234	41	0	.	113	71	124	71	77	71	109	72	32	100	121	75	52	100	0	.	0	.	29	100	88	71	122	76	94	59	360	48	334	54	0	.	69	71	81	71	45	100	0	.	122	58	56	100	110	60	146	50	54	100	258	49	37	100	166	53	163	58	26	100	20	100	55	72	104	53	0	.	136	47	89	58	42	100	59	71	28	100	76	71	0	.	102	71	0	.	33	100	82	71	0	.	95	71	0	.	0	.	0	.	0	.	90	72	115	100	27	100	695	28	164	49	216	45	42	100	130	72	28	100	102	61	108	71	0	.	36	100	0	.	0	.	12128	7
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	.	53	100	0	.	0	.	134	59	520	34	292	45	49	100	0	.	62	100	0	.	0	.	0	.	107	71	0	.	0	.	0	.	106	59	50	100	26	100	106	59	88	72	110	58	49	100	39	100	0	.	0	.	0	.	0	.	60	100	0	.	53	100	0	.	0	.	0	.	0	.	50	100	42	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	27	100	0	0	0	.	0	.	0	.	0	.	73	100	0	0	0	.	0	.	0	.	0	.	119	81	57	100	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	177	45	109	72	27	100	0	.	0	.	0	.	56	100	0	.	48	100	0	.	0	.	0	.	2830	13
	broadleaves	0	.	33	100	0	.	0	.	0	.	0	.	34	100	62	100	90	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	100	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	98	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	58	100	99	100	0	.	0	.	82	100	28	100	0	.	0	.	28	100	26	100	0	.	61	71	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	43	100	43	100	0	.	0	.	1096	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	33	100	0	.	53	100	0	.	0	.	167	51	581	32	382	42	49	100	0	.	62	100	0	.	0	.	0	.	107	71	0	.	123	100	0	.	106	59	50	100	26	100	127	52	88	72	110	58	49	100	137	77	0	.	0	.	0	.	0	.	60	100	0	.	53	100	0	.	0	.	0	.	0	.	50	100	42	100	0	.	53	100	0	.	0	.	0	.	0	.	0	.	53	100	0	.	37	100	58	100	99	100	0	.	27	100	82	100	28	100	0	.	0	.	28	100	99	78	0	0	61	71	0	.	0	.	45	100	119	81	57	100	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	177	45	109	72	65	72	0	.	0	.	0	.	56	100	0	.	91	71	43	100	0	.	0	.	3926	12
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	.	75	100	0	.	57	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	86	71	0	.	0	.	61	100	0	.	0	.	57	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	100	0	.	116	72	0	0	63	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	71	0	.	0	.	81	100	60	100	85	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	71	39	100	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1224	22
	broadleaves	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	73	100	0	.	87	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	.	41	100	69	71	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	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	39
	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	.	67	100	0	.	0	.	0	.	75	100	73	100	57	100	87	100	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	86	71	0	.	0	.	61	100	0	.	0	.	57	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	100	0	.	116	72	0	0	63	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	71	0	.	0	.	81	100	60	100	85	100	0	.	0	.	41	100	69	71	0	.	0	.	11	100	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	71	39	100	48	100	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1633	19
>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	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	183	61	51	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	.	69	100	0	.	0	.	0	.	0	.	0	.	96	100	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	590	33
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	242	85	0	.	192	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	.	485	49
	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	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	183	61	51	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	.	69	100	50	100	0	.	0	.	0	.	0	.	338	67	0	.	192	58	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1075	29
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	1424	33	2035	35	956	38	4239	21	2398	28	3925	23	17217	11	16099	11	14305	11	6584	14	420	74	447	56	1113	42	92	.	1268	39	3664	24	5290	22	3997	24	121	70	3963	24	6257	16	4214	18	6342	16	12365	12	11273	11	7003	15	3549	25	6560	18	6885	18	941	42	1913	25	1224	34	1055	38	3175	23	1321	32	1893	29	3150	28	2425	28	1499	39	2840	23	2203	30	4370	23	2400	23	1583	33	1179	33	1517	39	915	29	1898	27	3752	24	1893	29	1965	31	1549	29	2084	26	1223	32	2089	25	2098	27	198	57	43	75	108	54	1164	30	682	34	770	48	1133	36	1238	43	2104	30	1378	33	1303	37	1278	35	1219	39	475	79	1754	27	659	44	892	40	1841	21	955	40	1150	31	1696	27	390	62	1029	45	479	57	1442	51	8430	13	6231	15	6902	14	1003	35	936	35	2266	29	2862	24	2499	23	1469	32	1326	42	1302	34	0	.	268767	2
	broadleaves	3342	20	1856	26	831	35	2466	25	237	47	1004	31	3628	19	4048	18	7133	13	3755	15	1397	36	1149	34	1555	24	0	.	481	50	1089	29	1344	27	1471	30	588	47	1647	26	894	37	630	27	413	41	1238	24	286	41	5780	14	2119	33	1002	31	1460	26	428	42	374	35	420	34	341	45	361	33	794	35	143	70	1604	34	1210	32	1702	34	1422	26	1089	36	2641	29	2233	20	1402	30	1413	26	1323	30	1988	24	779	38	372	63	1082	32	1213	29	1135	34	1584	25	100	47	499	44	269	73	1038	26	1813	23	2548	17	1574	26	968	26	2246	23	618	38	60	93	170	51	840	34	630	48	459	42	851	45	119	64	1265	27	501	49	342	36	239	36	1080	31	1218	29	815	40	883	38	587	45	200	52	399	54	330	38	192	28	1235	22	465	43	1039	34	646	36	1868	25	1480	27	1088	32	925	35	723	37	0	.	112218	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	4766	19	3891	23	1787	30	6705	18	2635	28	4929	22	20845	10	20147	10	21437	9	10339	12	1817	34	1596	32	2668	26	92	.	1749	33	4753	21	6634	19	5468	21	709	45	5610	20	7150	15	4844	17	6754	16	13603	11	11559	11	12783	11	5669	21	7562	17	8345	17	1370	34	2286	24	1644	31	1396	34	3536	22	2115	28	2036	29	4753	23	3635	23	3201	28	4262	20	3292	26	7011	20	4633	18	2985	26	2592	25	2840	27	2903	23	2677	24	4123	23	2975	25	3179	25	2684	27	3668	22	1324	30	2588	22	2367	25	1236	24	1857	23	2657	17	2738	20	1650	23	3016	21	1751	29	1299	41	2273	29	2218	27	1933	31	1736	31	2070	34	594	66	3019	23	1160	41	1234	37	2080	21	2034	29	2367	25	2511	24	1273	35	1616	38	678	55	1841	43	8760	12	6423	14	8138	13	1468	31	1975	29	2912	27	4730	21	3979	20	2557	26	2252	31	2025	29	0	.	380985	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. 2298053. Birmensdorf, Swiss Federal Research Institute WSL

back