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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NFI1
growing stock (stemwood)
diameter at breast height (main diameter 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 1983/85
		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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
12-15 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	24	50	52	57	33	90	44	47	33	56	52	36	249	16	224	19	333	25	123	20	0	0	49	86	16	48	5	.	24	54	96	36	97	43	63	42	16	92	64	51	80	27	77	25	61	24	183	19	299	16	182	26	85	49	169	25	147	29	22	79	61	60	10	53	30	42	36	43	26	64	55	40	47	57	54	74	34	46	62	34	9	49	50	48	92	43	15	48	36	67	21	65	24	58	17	40	85	48	13	46	56	54	41	43	58	68	6	64	33	36	21	39	1	345	0	.	0	.	28	43	7	58	9	64	39	52	12	59	53	36	15	46	6	42	29	55	15	54	4	100	49	39	30	51	7	71	47	31	12	42	41	65	28	48	20	56	53	64	1	100	10	61	277	21	183	21	106	30	36	55	21	79	20	76	42	44	43	40	22	77	6	79	49	66	0	.	5212	4
	broadleaves	57	30	86	34	39	36	78	34	10	80	34	41	161	28	181	20	246	20	263	21	16	67	28	40	58	37	0	.	37	52	47	35	89	30	73	37	65	56	65	32	29	40	27	40	52	46	148	28	21	50	171	24	42	38	58	35	85	29	6	82	29	47	38	63	28	70	26	58	44	52	31	62	64	67	51	56	71	39	51	38	42	54	70	27	122	28	37	38	47	34	30	43	39	34	40	44	24	47	59	50	53	45	45	32	51	41	14	53	26	40	14	62	124	32	230	25	325	23	132	38	115	32	146	27	42	50	30	100	17	67	49	44	27	44	46	47	15	55	20	100	29	35	2	100	10	48	32	38	70	42	53	41	10	53	40	66	19	53	2	100	11	68	59	35	27	37	169	26	7	63	27	61	21	48	42	45	52	36	25	37	36	48	31	41	0	.	5506	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	80	28	138	31	72	48	123	28	43	54	86	32	410	16	405	14	579	17	386	17	16	67	77	57	73	33	5	.	61	44	143	31	186	27	136	29	81	52	129	32	109	24	104	25	113	25	331	17	320	15	353	19	127	37	227	24	233	23	28	64	90	43	48	52	58	44	62	38	69	40	86	34	111	46	105	47	105	32	113	33	51	45	120	27	214	25	52	31	83	37	51	38	63	38	57	36	108	46	72	44	108	36	86	33	108	41	20	43	59	27	35	34	125	31	230	25	325	23	160	32	122	31	156	27	81	49	43	73	70	33	64	36	33	38	75	39	30	42	24	85	77	34	31	50	17	48	79	30	82	38	93	36	38	40	60	49	72	55	3	73	20	54	336	19	210	19	276	20	43	47	48	49	41	51	84	37	95	28	46	41	42	46	80	45	0	.	10717	3
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	105	36	74	62	85	43	218	34	198	41	383	33	1003	13	872	15	1000	20	491	21	6	100	14	62	51	52	24	.	67	61	335	30	326	39	280	36	16	73	278	37	348	20	354	26	325	27	783	18	1037	17	587	29	205	50	372	25	543	26	59	47	169	42	109	40	79	46	146	32	142	42	222	36	194	36	170	33	75	57	273	34	58	49	174	29	233	44	17	53	110	44	64	57	71	40	55	33	300	38	109	35	158	40	182	42	35	53	6	73	159	30	89	38	5	82	0	.	0	.	58	38	25	59	39	74	107	49	72	50	236	43	27	59	50	54	141	54	66	63	28	95	117	32	146	46	73	56	225	32	31	48	102	40	135	36	10	61	109	54	29	85	37	86	824	19	771	17	697	25	41	48	5	112	117	45	77	44	219	34	43	42	15	71	66	44	0	.	18581	4
	broadleaves	262	37	176	27	134	42	268	35	36	63	208	35	425	20	574	18	874	16	818	19	92	40	232	38	193	42	0	.	98	60	183	33	225	40	206	40	113	61	287	33	103	39	78	38	118	55	314	33	53	47	552	18	147	31	139	45	239	32	56	48	116	43	44	47	58	63	54	39	224	41	27	68	190	35	184	39	181	37	208	30	248	42	296	34	423	27	128	61	171	39	156	42	283	28	114	46	14	66	160	41	126	35	114	47	232	30	36	67	60	33	104	57	280	37	443	27	693	22	251	33	375	32	444	23	94	46	19	72	26	89	110	45	98	50	66	42	121	50	17	100	118	38	14	59	47	40	40	54	167	40	224	36	117	57	161	39	41	56	9	100	45	64	90	38	50	44	276	23	40	79	50	49	45	39	221	40	200	37	89	41	183	36	87	44	0	.	16508	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	366	28	250	29	219	33	485	25	234	41	591	29	1428	11	1446	13	1874	13	1310	16	97	39	246	37	245	35	24	.	165	46	518	24	551	29	486	27	129	55	566	26	452	19	432	24	442	25	1097	16	1090	16	1139	18	352	33	511	23	782	22	116	38	285	33	154	36	136	42	199	30	367	32	250	33	384	26	354	29	256	31	481	25	306	36	470	26	657	25	145	55	281	30	221	35	353	27	168	34	313	37	269	32	284	30	296	36	267	28	42	58	219	25	193	36	285	36	443	27	693	22	309	28	400	30	483	22	201	41	91	50	262	40	137	37	148	47	207	47	187	42	46	70	236	28	160	45	120	45	265	30	198	35	326	30	252	34	170	39	150	46	39	69	81	53	913	18	821	16	974	19	81	47	56	50	162	35	298	34	420	28	132	36	198	37	152	34	0	.	35089	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	403	41	190	53	171	52	947	27	782	33	1187	31	3701	13	2678	15	2984	16	1907	18	17	100	55	60	283	36	0	.	284	49	723	37	1121	29	1309	30	0	.	800	35	1420	21	663	25	1133	26	2433	17	3260	15	1638	22	639	29	1760	21	1154	22	146	68	318	38	270	43	217	43	644	33	297	48	459	36	861	29	579	34	453	54	604	31	267	55	681	30	651	34	204	42	436	42	178	49	238	36	221	33	961	29	485	36	251	44	234	36	151	55	123	53	335	35	482	37	19	73	0	.	-8	100	174	42	142	49	121	59	363	41	359	58	586	34	127	54	379	57	369	41	189	47	229	92	285	27	324	60	237	48	471	27	72	42	277	37	402	35	23	100	97	51	108	55	449	61	1835	16	1561	17	1619	22	128	54	177	57	565	34	678	35	736	29	434	41	335	56	270	57	0	.	58521	3
	broadleaves	816	24	520	31	241	41	536	34	122	61	458	35	849	20	1006	22	1746	16	1009	21	246	45	325	40	492	32	0	.	168	74	321	39	319	36	542	35	64	57	522	34	178	48	164	39	161	60	247	35	8	100	1895	18	592	30	231	36	436	32	128	52	99	50	183	49	101	54	199	44	316	46	47	77	510	37	420	33	562	41	379	34	328	35	801	34	691	24	384	51	508	31	277	46	617	29	195	37	58	54	337	45	222	36	185	54	361	32	13	71	63	63	70	63	183	41	516	25	690	25	157	36	266	36	451	26	150	43	7	100	57	84	200	40	165	45	153	49	323	43	15	100	450	34	114	68	137	37	78	60	306	34	598	34	342	46	315	44	128	45	52	88	23	71	93	51	10	71	298	34	156	50	310	51	127	57	439	34	288	30	206	45	227	42	265	39	0	.	30559	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	1219	23	711	30	411	36	1484	22	903	33	1644	28	4550	12	3684	13	4730	12	2916	15	263	46	380	39	774	29	0	.	451	41	1044	29	1440	25	1851	25	64	57	1322	27	1598	20	827	22	1295	24	2680	16	3268	15	3534	15	1231	22	1991	20	1589	19	275	49	418	33	453	38	318	36	842	28	613	34	506	35	1371	24	999	27	1015	33	983	23	595	32	1483	25	1342	24	588	38	944	30	455	35	855	25	416	27	1019	28	822	30	473	32	419	33	512	33	135	48	398	31	552	33	202	38	516	25	682	26	331	28	408	31	572	25	513	33	366	57	643	32	328	33	544	43	521	34	512	40	244	88	735	25	438	49	374	39	548	25	378	31	875	29	744	30	338	43	225	43	160	54	472	58	1928	15	1571	17	1918	19	283	40	486	39	692	30	1117	27	1024	24	640	33	562	40	535	36	0	.	89081	2
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	732	44	1002	37	330	45	2345	23	1037	32	1820	26	7816	12	6256	13	5935	14	2332	17	188	68	23	82	580	44	40	.	479	46	1202	28	1580	25	1619	27	14	100	1415	24	2327	20	1481	21	2885	18	4244	14	4349	12	2410	17	1164	29	2612	21	2715	19	402	41	764	27	487	41	262	43	1094	25	540	39	729	33	1211	33	987	34	577	39	1184	31	998	33	1684	24	831	26	908	34	434	34	903	40	458	31	629	30	1542	27	664	30	810	38	816	34	652	31	391	40	671	26	681	28	87	61	11	100	114	66	209	49	284	46	239	51	281	43	375	43	570	31	513	45	561	34	532	31	519	41	88	86	618	30	82	64	380	44	701	25	335	43	473	43	800	33	199	72	237	51	117	66	463	54	2386	16	2160	18	2590	16	493	39	557	38	1137	33	1391	28	1042	27	670	37	661	41	570	38	0	.	104683	2
	broadleaves	1107	25	600	29	172	39	467	34	87	47	249	41	1168	25	1294	23	2672	17	786	20	261	40	380	39	465	41	0	.	126	78	245	40	223	41	351	39	101	100	468	29	399	53	173	37	21	68	131	42	62	67	2046	17	600	35	322	38	407	37	369	60	50	62	49	57	22	71	128	41	159	65	25	76	551	43	254	58	517	42	456	33	196	38	523	30	508	31	721	31	461	38	431	41	623	30	257	46	197	97	275	42	356	43	366	40	289	38	0	.	55	47	57	83	83	48	203	30	272	41	322	34	144	58	334	31	164	58	0	.	36	60	135	40	92	54	97	54	182	46	26	100	380	43	174	54	137	46	46	55	256	36	229	42	250	47	148	70	157	52	77	55	137	53	16	84	0	.	145	39	188	54	341	45	260	42	468	32	736	35	287	41	341	39	415	36	0	.	30558	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	1840	25	1602	27	502	35	2812	21	1124	31	2069	26	8984	11	7549	12	8606	11	3118	15	449	40	403	37	1045	32	40	.	605	43	1448	25	1803	23	1970	24	115	89	1883	21	2726	19	1654	20	2906	18	4374	14	4411	12	4456	13	1764	23	2934	20	3122	19	771	36	813	27	536	39	284	42	1222	24	699	34	755	33	1762	28	1242	30	1094	29	1640	25	1194	30	2207	21	1339	23	1630	27	895	29	1335	32	1081	26	886	27	1738	26	940	28	1166	31	1182	29	941	26	391	40	726	24	738	27	170	41	214	29	386	38	530	29	429	37	573	30	445	37	375	43	605	31	648	37	653	31	629	29	701	36	114	74	998	26	256	44	517	39	747	25	591	32	702	34	1050	29	347	53	394	44	194	56	600	45	2402	16	2160	18	2735	15	680	35	898	33	1397	30	1859	25	1778	26	957	31	1002	33	986	30	0	.	135241	2
=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	182	41	794	41	151	56	772	33	455	52	641	39	4623	14	4590	14	3587	17	1010	27	145	100	159	100	138	52	23	.	175	49	537	41	1202	31	484	35	0	.	1402	32	1973	22	1334	27	2203	21	3534	15	2224	18	937	24	1003	34	918	24	1122	29	287	46	286	36	350	46	531	47	958	35	193	48	681	42	728	31	492	46	692	48	1011	28	977	34	1589	35	367	35	688	39	163	59	263	56	42	100	786	35	471	35	813	37	343	43	340	46	614	33	496	42	805	38	352	55	41	71	24	100	127	100	472	46	186	49	245	58	325	52	334	57	369	48	481	34	413	48	317	37	661	50	0	.	739	43	110	64	320	55	470	30	457	43	152	54	377	44	59	73	304	73	150	85	88	62	2476	18	1080	21	1840	20	237	66	306	44	194	52	398	33	398	48	293	45	306	51	389	43	0	.	66774	3
	broadleaves	723	36	368	43	279	50	423	42	0	.	66	64	606	31	744	32	1071	26	139	58	508	57	142	55	154	47	36	.	44	100	141	53	83	72	280	67	46	100	84	78	246	60	158	71	12	100	110	60	26	100	679	29	379	50	260	77	60	72	0	.	0	.	-8	100	21	100	0	.	36	71	0	.	265	48	282	49	110	60	138	68	95	74	82	58	256	42	170	54	111	51	86	52	37	100	0	.	50	100	138	79	98	79	230	58	461	45	0	.	184	92	14	100	64	64	186	49	274	59	314	62	54	57	323	40	73	76	0	.	65	71	151	52	45	71	0	.	123	66	0	.	17	100	138	60	27	100	0	.	62	76	17	100	0	.	11	100	69	100	0	.	60	72	0	.	0	.	194	49	0	.	170	79	53	72	160	48	470	36	156	46	136	65	49	71	0	.	14186	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	905	32	1163	31	430	39	1195	28	455	52	707	37	5230	13	5334	13	4658	15	1148	25	653	49	300	59	292	35	59	.	218	44	678	39	1286	31	763	38	46	100	1486	31	2219	20	1493	25	2215	21	3644	15	2250	18	1616	19	1383	29	1178	27	1181	28	287	46	286	36	343	46	552	45	958	35	229	42	681	42	993	29	774	37	802	46	1150	26	1072	34	1671	34	623	30	858	34	273	43	349	44	79	100	786	35	520	33	951	35	441	39	571	42	1076	30	496	42	989	36	366	53	105	47	210	44	401	51	787	37	240	40	568	34	398	45	334	57	434	48	632	32	458	44	317	37	784	44	0	.	756	42	248	54	347	52	470	30	519	39	169	49	377	44	70	63	372	62	150	85	148	47	2476	18	1080	21	2035	19	237	66	476	43	247	44	559	30	868	34	449	35	442	46	439	40	0	.	80960	3
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	1447	32	2112	31	768	39	4327	21	2506	29	4083	24	17392	10	14619	11	13839	11	5863	15	356	71	300	63	1067	39	93	.	1028	43	2894	26	4327	23	3755	23	46	63	3959	22	6148	17	3909	18	6606	17	11176	12	11168	11	5754	15	3096	25	5831	18	5681	18	917	39	1598	24	1226	35	1118	35	2878	23	1197	34	2147	30	3042	27	2282	28	1831	37	3133	23	2310	28	4178	22	2174	23	1833	31	1180	31	1430	37	832	28	1708	27	3358	25	2084	28	1617	31	1614	30	1511	26	1021	34	2003	25	1624	28	153	59	35	76	233	85	941	33	644	36	653	47	1115	36	1153	42	1814	30	1163	35	1408	34	1388	31	1450	37	349	90	1808	28	692	45	1016	38	1913	22	908	36	1045	28	1742	29	311	60	800	45	406	54	1047	55	7798	12	5755	14	6853	14	934	37	1066	36	2034	30	2586	26	2438	22	1461	32	1322	38	1344	31	0	.	253772	2
	broadleaves	2964	21	1751	25	866	34	1773	25	255	49	1014	31	3209	19	3799	17	6609	13	3015	16	1121	36	1107	32	1362	27	36	.	473	59	938	31	939	28	1451	30	390	54	1426	26	955	40	601	31	364	49	950	26	171	44	5343	14	1760	27	1009	31	1227	29	559	52	294	35	306	38	230	49	406	33	780	37	131	60	1579	34	1190	34	1441	34	1234	28	910	33	1772	26	2001	19	1440	34	1298	25	979	31	1599	25	606	35	342	74	969	34	855	33	940	33	1394	27	63	56	388	51	259	57	733	27	1577	23	2253	18	1176	27	953	26	1698	23	524	37	56	82	200	55	646	36	428	42	362	41	764	42	78	71	995	29	441	53	359	34	196	35	860	30	1121	28	719	37	674	38	413	44	140	55	274	56	257	36	87	37	1084	23	390	43	898	38	506	41	1331	26	1745	26	763	35	924	32	847	33	0	.	97317	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	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2293320. Birmensdorf, Swiss Federal Research Institute WSL

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