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
number of stems
diameter at breast height (main diameter classes) · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 n
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 (main diameter classes)	conifers and broadleaves	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.
12-15 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	404	37	499	45	490	41	414	29	278	32	331	43	3356	11	2110	12	2463	18	1427	18	97	71	153	58	186	35	0	.	372	50	631	25	906	21	728	33	10	100	754	23	1566	21	1052	19	1199	17	2861	12	3237	12	1773	15	886	26	968	26	1333	16	135	59	392	27	99	44	173	34	506	26	356	43	698	25	737	30	573	30	542	36	734	22	305	41	539	29	665	35	397	41	511	42	300	54	313	32	517	34	814	27	224	38	457	35	342	40	784	39	154	42	718	25	310	30	38	61	11	100	0	.	350	28	257	35	188	45	427	30	119	41	609	32	220	33	128	37	623	30	264	55	144	64	553	26	199	43	101	40	571	22	369	44	431	38	216	43	153	77	198	44	52	67	98	63	2842	13	2186	16	2037	14	460	39	135	59	241	43	694	38	480	51	329	52	78	50	567	44	0	.	59143	3
	broadleaves	991	21	715	27	366	25	620	22	271	53	270	38	2055	17	1095	15	1769	14	1436	16	287	36	322	34	521	26	0	.	236	36	267	30	589	25	647	25	291	43	791	24	395	27	364	26	899	26	2070	19	613	36	1822	15	458	34	535	30	892	21	217	41	296	30	108	45	206	49	252	27	399	53	322	44	598	30	269	41	693	30	786	25	546	27	705	29	783	21	321	32	437	27	353	31	533	23	603	28	331	36	262	39	409	28	415	32	363	35	521	38	432	25	366	30	1849	22	1253	21	3467	16	2035	18	1234	21	1669	20	257	32	349	59	149	45	402	35	460	32	476	25	273	33	161	59	374	28	241	42	216	46	270	29	304	33	266	28	322	33	353	45	168	43	53	72	266	54	931	20	956	24	2080	16	198	34	180	37	177	46	259	28	310	37	413	29	174	29	155	35	0	.	55811	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	1395	20	1213	25	856	27	1034	21	549	34	601	31	5412	10	3205	11	4231	13	2863	14	384	37	475	30	707	24	0	.	608	36	898	21	1495	19	1375	23	300	42	1546	19	1961	18	1416	16	2098	16	4931	11	3851	12	3595	12	1344	23	1503	21	2225	15	352	34	688	21	206	37	379	36	758	22	755	35	1020	26	1335	22	842	26	1235	25	1520	20	850	24	1244	23	1447	20	718	30	947	28	654	32	846	22	1120	24	1145	23	486	29	866	24	757	26	1147	29	675	33	1149	20	676	22	1887	21	1264	21	3467	16	2384	16	1491	20	1856	18	685	25	468	45	759	29	622	26	587	27	1099	22	537	37	305	47	927	21	440	30	317	36	841	20	672	29	697	27	538	33	506	40	365	35	106	55	363	44	3773	11	3142	14	4116	12	658	30	315	33	418	32	953	30	790	35	743	29	253	27	723	38	0	.	114954	2
16-23 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	409	29	542	44	732	32	534	33	422	31	943	26	4955	10	2490	13	2965	16	1520	18	85	61	207	82	241	31	20	.	631	41	1105	25	1363	20	897	30	108	82	1011	24	1803	22	1631	18	1489	15	4027	11	4777	10	1967	17	868	27	1188	24	1918	17	260	65	656	30	253	37	217	33	634	26	685	33	839	28	858	24	623	30	593	33	1149	23	378	41	739	19	363	36	395	30	399	39	380	47	266	33	555	35	1159	22	343	27	577	30	444	38	571	43	162	36	789	21	493	28	124	58	9	100	30	100	528	25	270	41	234	36	498	29	398	33	734	27	401	28	198	40	827	26	301	31	97	62	520	31	325	35	183	32	701	22	246	34	371	44	436	34	104	43	380	47	79	71	211	58	4281	12	3395	13	2707	14	533	34	223	52	290	33	535	35	527	31	416	36	89	45	520	36	0	.	77351	2
	broadleaves	1025	17	800	24	542	28	1069	23	251	37	493	26	1990	15	1594	14	2343	12	2497	14	242	41	471	30	843	29	0	.	338	30	534	25	885	27	590	26	461	49	1087	25	579	26	580	26	536	28	1879	17	806	40	2110	13	614	27	432	22	916	22	235	40	292	30	208	37	152	38	227	28	502	31	228	40	817	24	378	29	579	32	779	23	669	36	1020	21	1085	20	353	25	696	28	557	29	898	23	864	26	375	36	421	37	467	36	383	31	396	24	349	35	643	25	231	44	1489	22	1921	19	3376	15	2173	17	1855	21	2392	17	433	36	157	48	181	42	624	34	608	32	639	24	458	35	145	49	658	27	255	41	293	42	279	33	510	38	554	30	452	31	413	37	221	35	55	52	241	40	702	23	703	24	2088	15	207	34	311	42	325	33	481	30	369	27	382	31	285	34	187	35	0	.	66731	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	1433	16	1342	24	1273	24	1602	19	673	26	1436	23	6945	9	4085	10	5308	11	4017	12	327	35	678	34	1084	26	20	.	969	32	1639	20	2248	17	1487	22	568	45	2098	19	2382	18	2210	16	2024	14	5906	9	5583	11	4077	11	1482	21	1620	20	2834	15	495	39	948	25	462	30	370	30	861	21	1187	24	1067	26	1676	18	1001	24	1173	24	1928	19	1046	29	1758	18	1448	18	748	23	1095	24	937	27	1164	21	1419	22	1534	21	764	26	1044	24	827	28	967	28	511	28	1433	17	725	24	1613	20	1931	19	3406	15	2701	14	2126	20	2627	16	931	27	555	30	915	24	1025	24	806	31	1466	22	759	26	242	42	1178	23	579	29	476	30	979	22	756	29	925	26	889	25	517	32	600	36	135	53	452	36	4983	11	4099	11	4795	11	740	28	534	34	615	24	1016	25	897	22	798	25	374	30	707	31	0	.	144082	2
24-35 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	388	31	241	34	574	29	475	25	797	26	905	26	5329	10	2799	11	2390	13	1822	15	30	58	92	42	286	28	69	.	314	36	842	27	1574	18	783	23	19	100	1054	35	1997	15	1701	16	1618	17	4093	11	4355	10	2032	15	830	24	896	18	1751	17	199	38	476	29	337	35	186	30	764	24	549	28	808	24	817	25	725	24	426	36	1091	19	297	32	952	20	520	27	173	33	354	29	197	43	404	29	686	23	1020	23	319	30	577	30	336	32	166	47	228	37	686	25	582	29	59	47	52	63	89	100	561	23	206	41	318	37	427	28	319	41	707	28	175	36	301	33	901	24	198	41	99	73	439	26	314	40	231	35	893	20	172	35	331	30	437	29	27	74	224	47	99	41	428	38	3949	11	2818	12	2939	12	334	34	307	39	442	34	394	27	562	27	232	35	252	47	156	34	0	.	74325	2
	broadleaves	1227	18	546	23	397	25	793	25	155	34	557	24	1620	17	1499	16	1993	13	1784	13	237	37	691	25	805	22	0	.	234	40	487	30	748	32	616	25	208	36	712	27	295	34	288	36	267	40	782	22	198	37	2392	12	728	26	321	34	621	24	161	34	282	38	235	41	146	44	228	26	573	27	34	75	440	28	580	26	683	31	685	23	771	23	1011	21	872	19	512	28	748	22	362	30	1084	20	440	25	124	44	344	30	574	24	297	34	596	24	131	56	300	31	190	42	737	24	1243	18	1649	16	841	22	626	22	1298	19	242	39	30	58	59	46	499	32	286	38	347	30	362	33	19	100	646	27	202	38	320	37	123	45	510	24	675	25	394	35	330	32	152	38	43	61	390	39	291	33	151	31	840	19	211	33	277	38	211	27	669	24	521	23	307	33	252	34	99	45	0	.	49451	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	1615	16	787	19	971	21	1268	19	952	25	1462	21	6949	9	4298	10	4382	10	3606	11	267	36	783	24	1091	20	69	.	547	27	1329	21	2322	17	1399	18	227	37	1766	24	2293	14	1989	15	1885	16	4874	10	4553	9	4425	10	1558	19	1217	18	2372	15	360	29	758	24	572	29	332	28	992	21	1121	21	842	24	1258	20	1306	19	1109	24	1776	16	1068	21	1963	16	1392	18	685	24	1102	20	559	25	1488	18	1126	19	1144	21	663	23	1152	21	633	25	763	23	358	31	986	20	772	24	795	22	1295	18	1737	16	1402	17	832	20	1616	17	669	25	348	39	767	27	674	26	587	28	1249	21	560	28	118	65	1085	20	516	31	551	31	1016	19	682	22	1006	20	831	23	357	30	376	36	141	40	818	28	4240	10	2969	12	3779	11	545	26	584	28	653	26	1062	21	1083	20	539	25	503	31	255	30	0	.	123776	1
36-51 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	346	24	375	28	179	32	827	18	420	25	885	19	4283	9	3124	9	2187	10	1473	12	117	44	121	41	275	30	8	.	407	29	673	21	1039	15	776	22	4	100	725	19	1263	13	1074	14	1428	14	3019	9	3055	9	1566	12	569	21	1229	16	1383	15	208	33	452	21	265	31	171	30	556	19	277	26	537	22	647	20	681	21	272	33	742	17	370	27	998	17	444	21	336	24	305	27	253	34	294	22	443	22	877	18	292	24	519	25	283	34	256	26	222	33	504	20	463	23	42	54	33	71	25	48	320	22	142	31	160	38	194	32	206	38	343	25	318	32	285	26	626	20	343	28	167	48	350	24	176	39	232	27	539	18	278	27	295	27	437	22	71	50	164	47	102	49	381	34	2266	10	1494	12	1833	11	338	28	268	34	492	24	558	21	552	20	216	30	311	28	263	34	0	.	59319	2
	broadleaves	687	16	424	21	201	28	445	22	118	35	288	28	721	17	561	15	1286	11	729	15	221	29	309	30	457	23	0	.	184	38	162	31	243	28	346	26	83	56	397	20	162	33	154	29	52	35	175	23	42	59	1404	13	451	29	172	27	282	25	172	32	55	37	113	42	63	39	114	27	160	34	55	49	261	32	237	25	289	32	306	24	327	25	480	26	406	20	377	22	293	23	297	26	532	19	268	28	56	64	173	29	176	29	140	31	229	22	12	58	120	27	51	65	176	27	359	22	313	23	304	21	211	25	310	22	85	41	25	72	24	56	127	35	132	41	146	31	158	34	13	100	233	26	188	35	68	36	52	40	140	29	339	24	198	30	179	35	104	37	44	47	198	35	55	42	21	63	166	28	176	29	154	28	236	27	305	23	297	24	221	27	178	33	149	33	0	.	22606	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	1033	14	799	19	380	22	1271	15	537	23	1173	18	5004	8	3684	9	3474	8	2203	10	338	27	430	26	732	20	8	.	592	25	835	19	1283	14	1122	19	87	54	1122	16	1425	13	1228	14	1481	14	3194	9	3097	9	2970	9	1020	18	1401	16	1665	13	380	24	507	20	379	27	234	27	670	17	437	22	592	21	908	18	918	18	561	24	1049	16	697	21	1478	16	850	17	714	18	598	20	549	23	826	17	711	19	933	18	465	20	695	21	423	27	485	21	234	32	624	17	515	22	217	24	392	21	338	22	624	15	354	21	470	20	280	27	231	37	367	24	445	26	417	23	773	19	500	24	180	46	583	20	365	28	300	26	591	18	418	22	635	20	634	19	250	32	268	35	147	41	579	26	2321	10	1515	12	1999	11	514	24	422	26	727	21	863	18	849	18	437	24	489	24	412	27	0	.	81926	1
=52 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	94	29	132	34	96	31	343	20	102	39	290	24	1637	10	1519	10	1259	10	483	18	46	77	43	48	62	36	0	.	152	32	170	26	349	21	211	25	8	71	304	23	580	15	572	17	702	14	1262	10	971	12	550	14	331	25	400	18	431	18	74	37	164	26	59	40	89	41	334	20	81	37	198	23	228	25	217	27	134	39	338	20	204	27	658	19	127	25	193	28	40	37	86	43	38	45	250	24	278	22	211	28	160	29	88	36	140	28	166	27	297	21	178	28	15	61	20	52	4	100	155	22	69	33	113	40	99	43	110	42	144	32	175	28	142	27	253	23	135	31	43	58	191	28	62	45	130	33	102	22	130	32	136	31	133	28	21	68	95	43	38	48	92	42	1128	12	553	16	713	15	145	33	55	47	130	29	167	25	169	27	97	34	106	37	121	34	0	.	24121	2
	broadleaves	251	21	134	26	78	28	117	30	13	58	58	36	291	28	148	27	375	17	85	26	145	32	52	35	139	33	0	.	39	49	36	37	77	42	106	41	16	100	97	29	50	53	23	47	12	58	84	31	8	71	262	18	100	35	57	50	46	44	35	52	8	71	24	71	8	71	8	71	41	42	5	100	94	45	78	33	46	37	48	43	61	38	115	29	110	28	139	29	20	53	44	42	83	38	47	44	21	100	74	39	70	40	55	44	89	29	0	.	113	41	26	74	65	41	125	33	121	33	163	24	45	49	155	25	22	74	9	100	8	71	50	47	28	48	4	100	56	41	0	.	16	61	84	44	4	100	0	.	27	63	32	43	28	43	8	71	45	56	9	70	99	42	8	71	0	.	54	39	21	53	79	43	125	33	87	33	118	39	36	40	43	47	44	38	0	.	6172	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	345	19	266	22	173	22	460	18	115	37	348	22	1928	10	1668	10	1635	9	569	16	191	30	95	33	201	26	0	.	190	28	206	24	427	19	316	23	24	71	402	19	629	15	595	16	714	14	1346	9	979	12	812	12	432	22	457	17	477	18	109	30	173	26	83	35	96	38	342	20	122	29	202	22	322	23	295	22	179	32	386	18	266	25	773	17	237	20	332	24	59	34	129	32	121	32	298	22	299	21	285	26	230	26	143	31	229	23	166	27	410	19	204	26	80	35	145	29	125	32	318	16	115	28	268	22	120	38	119	39	152	32	225	26	170	25	257	23	191	28	43	58	207	26	146	33	135	33	102	22	157	28	169	28	160	26	28	53	140	36	47	50	190	32	1136	11	553	16	767	14	166	31	133	32	255	23	254	22	287	25	133	28	148	31	164	29	0	.	30293	2
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	1641	21	1790	29	2070	26	2592	17	2019	23	3354	19	19561	8	12042	8	11264	10	6727	12	376	38	617	46	1050	23	97	.	1875	29	3420	20	5232	15	3394	19	148	79	3849	20	7209	14	6030	13	6436	12	15261	8	16394	8	7889	11	3485	18	4681	15	6816	13	876	36	2140	21	1014	29	836	26	2794	18	1947	26	3079	21	3288	19	2819	20	1967	26	4055	16	1553	26	3886	16	2118	20	1494	21	1608	26	1215	33	1315	24	2452	21	4148	18	1390	22	2291	23	1494	28	1918	29	932	25	2995	17	2027	21	278	41	125	61	147	84	1914	18	944	27	1013	29	1645	24	1152	30	2537	23	1289	23	1054	23	3231	22	1240	27	550	42	2052	20	1075	28	877	27	2806	17	1194	25	1565	26	1658	23	376	48	1060	38	371	43	1208	31	14466	9	10446	11	10229	11	1809	26	988	30	1595	22	2347	23	2290	22	1291	29	835	29	1627	29	0	.	294259	1
	broadleaves	4181	14	2619	19	1583	20	3044	18	807	33	1666	23	6677	13	4897	12	7767	10	6531	12	1132	28	1844	25	2765	19	0	.	1030	29	1486	23	2543	22	2305	19	1058	39	3085	20	1482	23	1409	22	1766	24	4989	16	1668	33	7990	10	2351	22	1517	20	2758	18	820	29	933	25	688	29	575	38	829	21	1674	28	644	38	2210	23	1542	23	2290	26	2604	19	2374	24	3330	19	3256	16	1703	20	2193	20	1612	23	3130	17	2223	22	906	30	1273	26	1697	23	1290	24	1673	19	1013	32	1608	21	864	32	4315	18	4902	16	8926	13	5515	16	3972	19	5824	16	1039	31	569	50	421	38	1701	29	1514	29	1613	22	1307	28	338	44	1927	21	971	29	901	33	723	25	1491	26	1866	23	1393	25	1283	30	689	31	204	49	1194	31	1986	21	1831	22	5227	14	813	26	1000	29	1073	24	1800	21	1615	20	1359	24	932	27	634	27	0	.	200771	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	5821	13	4409	17	3653	19	5636	14	2826	22	5020	18	26238	7	16939	8	19031	8	13258	10	1508	25	2461	23	3815	18	97	.	2906	23	4906	17	7774	14	5699	15	1206	38	6933	16	8690	12	7439	12	8201	11	20250	8	18062	8	15879	8	5837	16	6198	14	9574	12	1696	25	3073	18	1702	25	1411	26	3623	17	3622	21	3723	20	5499	16	4361	17	4258	20	6659	15	3927	20	7216	15	5374	15	3196	17	3801	18	2828	21	4445	16	4675	18	5054	17	2663	19	3987	18	2784	21	3591	19	1944	22	4603	14	2891	18	4593	17	5027	16	9073	13	7430	13	4917	17	6837	14	2684	22	1721	27	2959	22	2991	20	2568	22	4843	19	2547	22	888	34	3979	17	2046	24	1778	24	3530	16	2684	20	3431	19	3051	19	1658	27	1750	29	575	39	2402	24	16453	8	12278	10	15456	9	2623	22	1988	23	2668	18	4148	18	3905	17	2650	21	1766	23	2261	25	0	.	495030	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. 2297222. Birmensdorf, Swiss Federal Research Institute WSL

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