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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NFI4
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 2009/17
		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	32	51	60	60	81	71	29	40	-12	156	36	45	281	19	137	32	258	24	115	27	4	75	16	60	17	40	0	.	11	64	42	35	61	39	79	78	0	.	34	31	127	32	68	27	89	26	259	25	304	25	108	23	85	39	129	29	85	28	25	65	35	41	18	60	18	52	58	49	22	61	78	39	56	49	21	42	21	66	43	43	16	52	27	42	43	48	29	44	12	100	7	62	16	55	33	48	63	62	16	43	31	34	91	53	26	51	36	48	49	49	25	34	1	100	0	.	0	.	35	46	9	77	13	68	33	38	13	54	50	39	21	48	27	49	42	44	12	57	8	100	53	41	15	53	15	54	44	28	21	60	36	48	19	47	22	65	14	59	0	.	26	91	261	24	235	29	113	21	27	49	30	57	20	39	25	37	34	38	10	62	34	91	19	72	0	.	4785	5
	broadleaves	19	171	30	77	61	69	27	28	6	61	13	87	197	32	80	25	142	21	142	20	22	62	39	46	52	31	0	.	57	94	64	56	44	29	18	37	6	61	44	29	31	40	36	41	116	38	195	46	26	58	131	22	41	30	32	41	81	29	48	49	35	38	2	714	16	74	16	51	22	42	11	58	34	43	21	39	39	42	70	34	62	48	40	29	87	34	61	37	56	45	69	73	11	103	60	46	7	54	14	183	51	49	83	62	29	45	51	55	37	77	22	44	139	31	92	36	234	25	137	30	125	33	161	26	32	39	23	75	7	62	62	48	50	38	23	59	22	56	8	62	17	56	9	81	36	65	32	52	24	51	28	55	23	48	19	60	10	82	0	.	7	71	57	30	84	27	194	36	6	62	33	65	-20	204	31	33	23	39	22	40	7	59	43	42	0	.	4606	5
	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	.	0	0
	total	51	78	90	56	142	50	56	28	-6	333	49	44	478	18	217	23	400	18	257	19	26	53	55	44	70	26	0	.	67	80	106	37	106	28	97	65	6	61	78	23	158	27	105	23	205	25	454	24	329	23	240	17	126	30	161	25	165	21	73	40	70	31	19	87	34	46	74	40	44	38	89	35	90	36	43	35	60	43	113	30	78	44	67	28	130	29	90	31	68	42	76	67	28	62	93	39	69	58	29	86	82	36	174	41	55	35	87	41	87	45	47	31	139	31	92	36	234	25	172	27	133	32	173	25	65	32	36	62	58	37	83	38	77	36	65	40	34	44	15	78	70	38	24	61	51	55	76	30	45	39	64	40	42	35	41	53	24	65	0	.	34	74	318	21	319	23	306	25	33	43	63	43	0	14262	56	28	57	29	32	33	42	76	62	43	0	.	9391	4
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	100	46	100	59	102	68	58	49	66	43	168	36	618	15	596	21	816	20	355	25	0	.	47	69	52	49	0	.	38	62	260	38	252	28	171	48	0	.	105	59	331	30	293	25	270	26	790	17	947	15	403	23	247	35	336	28	325	22	55	71	148	41	41	48	60	42	158	43	181	50	233	31	147	41	83	48	165	53	208	32	40	53	68	45	225	37	91	47	43	126	28	46	51	50	111	32	184	42	57	45	149	38	151	46	272	45	107	71	190	31	96	38	0	.	0	.	2	100	153	43	35	52	35	95	122	41	66	55	204	36	61	55	31	42	105	47	104	58	42	53	106	39	46	61	23	81	183	29	118	63	145	52	50	46	102	76	65	65	0	.	14	72	870	20	784	23	464	26	96	47	71	72	61	88	162	45	127	74	56	90	20	61	79	52	0	.	15791	4
	broadleaves	168	28	95	29	100	52	235	50	15	62	130	41	469	21	359	21	493	17	555	21	73	45	56	63	166	40	0	.	75	39	149	31	254	37	260	60	43	52	219	28	47	39	104	36	211	46	295	25	121	48	476	21	126	29	78	44	204	27	74	44	91	43	38	57	49	46	65	36	119	53	12	56	176	35	115	39	120	34	181	31	322	38	125	37	239	35	93	39	146	35	77	56	133	33	159	43	34	42	99	47	130	42	48	49	139	30	86	49	197	41	77	49	449	29	459	32	770	22	412	27	441	29	445	29	141	38	44	93	53	59	109	43	94	40	101	43	62	75	60	67	118	38	8	100	33	46	88	37	103	43	98	37	67	43	134	49	58	64	8	100	16	75	159	30	194	31	414	22	32	79	71	40	81	41	90	34	79	34	187	47	89	41	164	56	0	.	14648	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	267	29	195	37	202	44	293	42	81	37	298	29	1087	13	955	17	1309	15	910	18	73	45	102	40	218	33	0	.	113	41	409	28	507	25	431	42	43	52	324	28	378	28	398	23	481	26	1085	14	1067	15	879	16	374	26	414	26	529	19	129	41	238	32	79	38	110	36	224	35	300	37	244	30	323	28	198	36	285	37	389	26	363	37	192	31	464	26	183	34	189	40	105	45	184	29	270	32	218	37	157	42	279	29	198	37	411	34	194	46	387	27	174	31	449	29	459	32	771	22	564	24	475	28	480	28	263	33	111	50	257	31	170	35	125	34	207	40	165	49	102	54	223	28	53	53	56	44	270	28	221	40	243	35	116	37	237	46	123	57	8	100	30	61	1029	17	978	20	878	18	128	41	142	41	142	51	252	34	206	47	243	42	109	36	243	41	0	.	30439	3
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	189	45	295	52	147	46	94	70	487	34	582	34	2604	14	1675	16	1266	21	1324	25	4	100	86	75	67	54	2	.	364	51	794	37	380	38	486	41	67	87	664	42	1109	22	859	24	854	24	2263	16	2636	13	928	23	477	27	796	28	1068	24	220	70	466	44	140	40	155	50	365	34	501	40	516	34	560	29	233	46	288	50	489	37	234	46	366	29	592	32	252	44	137	50	68	94	144	38	200	38	628	33	198	39	553	38	338	42	423	43	54	53	499	32	477	34	62	71	0	.	0	.	362	33	135	54	165	62	365	41	270	50	549	36	239	34	225	37	416	55	254	65	139	69	504	35	87	62	72	64	625	31	184	43	262	50	185	50	115	65	86	52	146	72	70	79	1882	16	1869	18	1502	18	179	45	183	57	141	43	441	38	571	34	180	49	171	68	173	38	0	.	44473	3
	broadleaves	324	33	245	35	295	38	518	37	55	74	445	33	963	21	923	20	1392	19	1560	19	157	53	258	42	539	30	0	.	107	57	349	39	687	32	336	36	126	60	312	31	206	42	119	46	108	50	705	28	190	48	1079	20	313	33	415	39	523	29	102	50	175	41	88	59	121	49	128	40	431	41	10	100	536	37	290	37	357	44	363	37	385	40	669	28	690	27	237	40	385	35	244	38	572	35	256	35	92	41	286	37	546	38	229	40	224	34	30	53	173	37	117	61	428	30	801	27	1063	22	404	33	723	29	638	26	390	41	18	71	58	89	249	48	200	54	218	42	192	57	16	71	489	33	22	76	61	72	93	51	308	40	254	36	266	48	235	38	103	59	46	76	70	67	133	44	88	42	518	26	91	54	148	48	154	38	440	31	308	33	195	42	234	48	195	36	0	.	30838	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	514	26	540	35	442	35	612	34	542	34	1028	27	3568	12	2598	14	2658	14	2884	17	161	51	344	36	606	30	2	.	471	45	1143	29	1067	25	822	29	193	57	976	32	1315	20	979	22	962	22	2968	14	2826	13	2008	15	791	23	1211	27	1591	20	322	52	641	35	227	38	276	39	493	29	933	32	527	34	1096	25	523	31	645	34	853	28	618	32	1035	24	1282	22	490	32	523	33	312	37	716	33	456	27	720	31	484	31	1099	28	566	33	647	31	84	43	672	27	594	30	491	28	801	27	1063	22	766	24	858	26	803	25	755	35	288	47	607	34	488	33	425	36	634	43	446	49	155	65	994	27	109	64	133	50	718	29	492	30	516	34	451	36	349	34	189	50	192	62	139	57	2016	15	1956	17	2020	16	270	36	331	38	295	35	882	27	880	25	375	36	404	40	368	32	0	.	75312	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	316	38	61	55	324	46	786	30	1191	30	1823	27	6425	12	3795	13	3194	16	2873	17	39	75	57	79	365	41	106	.	432	44	1160	31	1154	28	998	32	43	74	1391	25	2901	17	1377	20	2352	21	4343	14	5009	12	2796	19	777	32	1811	20	2098	21	217	50	841	29	429	38	374	40	1131	28	450	36	449	34	951	30	808	32	530	40	938	27	577	40	900	26	933	27	315	33	573	38	210	54	347	39	780	33	928	29	759	33	476	34	372	44	479	33	322	45	691	25	1073	30	102	61	0	.	9	100	424	30	250	39	258	46	397	41	511	53	902	33	420	50	675	39	592	38	277	44	197	51	528	33	136	54	229	46	913	26	223	49	502	32	656	30	256	83	407	48	130	72	117	72	2923	16	2456	16	3024	17	210	49	283	53	536	35	759	28	1137	27	282	42	432	54	328	43	0	.	88331	2
	broadleaves	1042	25	476	27	344	44	869	28	156	48	484	40	1295	23	1298	19	2376	15	1440	19	239	59	643	39	615	30	0	.	256	77	610	32	584	34	788	31	242	60	750	28	408	44	196	38	119	55	364	30	102	58	2535	15	1159	44	475	35	633	32	247	42	157	48	163	47	167	40	284	33	384	40	42	53	571	36	464	31	739	41	404	29	548	33	1032	26	1091	24	447	37	628	31	640	32	792	27	400	40	95	91	474	38	405	35	426	34	546	28	8	100	179	37	127	58	171	34	597	25	890	25	376	34	314	32	642	28	277	42	10	100	84	59	522	43	66	109	185	52	196	43	167	66	572	29	154	60	97	57	102	49	346	34	825	33	325	45	443	39	129	56	121	51	123	63	92	51	29	73	282	42	252	43	396	37	315	30	497	26	616	29	370	36	454	45	186	38	0	.	43182	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	.	0	0
	total	1358	22	537	26	668	34	1655	22	1347	30	2308	24	7720	11	5093	12	5569	12	4313	14	277	55	700	38	980	27	106	.	688	41	1770	24	1739	24	1786	23	285	56	2142	22	3310	16	1573	19	2472	20	4707	13	5111	12	5331	13	1937	30	2286	19	2731	19	465	34	998	27	592	35	541	36	1415	25	834	31	491	31	1522	25	1273	28	1269	30	1342	22	1126	29	1932	21	2023	21	762	27	1201	28	850	30	1139	25	1180	27	1022	27	1233	27	881	29	798	33	1025	25	330	44	870	22	1200	27	273	32	597	25	899	25	800	23	564	29	900	26	674	32	522	52	986	32	942	34	741	36	777	32	473	38	364	45	1100	24	290	56	327	44	1015	24	568	33	1327	26	981	26	699	45	535	43	251	55	240	55	3015	16	2485	16	3306	16	462	38	679	36	851	29	1257	24	1753	24	653	32	886	37	514	33	0	.	131513	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	260	41	205	48	404	53	2076	27	1147	37	1832	29	7095	13	6776	14	5832	15	2007	19	21	100	238	88	405	49	70	.	252	61	976	37	1790	29	1044	31	0	.	2217	26	3126	21	1929	22	3312	19	5070	15	4124	16	2489	20	1359	38	2354	25	2536	26	506	47	964	37	559	49	552	42	1421	26	519	44	485	46	1092	33	922	46	382	50	1366	32	1803	31	2910	28	690	34	802	47	263	51	253	66	97	61	1626	31	937	32	881	37	264	48	524	44	824	36	862	37	1366	32	920	35	91	62	64	71	126	55	602	35	341	40	473	60	553	42	517	45	568	46	739	36	812	35	962	36	370	48	42	71	875	41	59	79	377	58	616	31	651	44	395	38	854	40	136	59	275	51	310	80	328	71	4107	17	2225	19	3222	19	253	51	779	41	442	36	953	35	639	35	514	65	282	55	394	52	0	.	109683	3
	broadleaves	1011	27	674	37	275	43	1008	34	46	75	127	47	1059	39	1293	28	2152	21	462	30	486	45	176	56	463	39	0	.	70	74	408	51	155	58	491	34	87	100	422	37	270	55	272	42	49	59	291	41	47	74	1338	23	704	36	333	42	340	50	161	80	0	.	29	100	0	.	27	100	126	85	0	.	497	43	427	54	205	45	430	50	455	45	551	33	579	38	785	35	125	78	570	40	304	40	184	51	120	100	449	57	451	47	757	51	487	39	0	.	169	61	25	100	287	37	388	36	400	37	450	45	113	64	606	36	169	65	0	.	83	71	300	48	333	70	77	78	287	51	0	.	209	57	188	75	0	.	54	61	161	44	150	74	288	49	116	65	251	67	123	59	328	61	17	108	0	.	280	40	151	75	539	45	185	72	419	46	366	54	467	51	235	49	379	41	0	.	30827	5
	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	1271	26	879	32	680	37	3084	22	1193	36	1959	28	8155	13	8069	13	7984	13	2470	17	507	43	414	64	869	32	70	.	322	50	1383	31	1945	27	1535	25	87	100	2639	23	3396	20	2201	21	3361	19	5361	14	4170	16	3827	15	2063	28	2687	23	2877	24	668	42	964	37	588	47	552	42	1448	25	645	39	485	46	1589	28	1349	37	588	37	1796	28	2257	28	3461	25	1269	26	1588	32	388	45	823	37	400	38	1810	29	1056	30	1331	34	715	37	1282	40	1311	31	862	37	1536	30	946	34	378	33	452	32	526	33	1052	28	454	34	1079	33	722	37	517	45	651	46	1040	33	1145	33	1039	37	657	42	42	71	1083	35	247	62	377	58	670	31	812	38	545	36	1142	34	252	44	526	42	433	68	657	49	4125	17	2225	19	3502	18	404	44	1319	36	627	34	1372	31	1005	32	981	44	517	39	773	37	0	.	140509	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	898	29	721	36	1059	37	3043	24	2879	29	4442	24	17024	11	12979	12	11365	12	6673	15	68	72	444	60	907	40	177	.	1096	41	3231	25	3638	23	2778	26	110	79	4411	22	7594	15	4527	18	6877	17	12725	12	13019	11	6725	16	2946	25	5426	19	6112	19	1024	39	2453	25	1186	35	1159	35	3133	23	1674	32	1761	32	2805	26	2068	32	1386	35	3044	24	2670	28	4271	23	2483	22	1490	32	1029	34	567	51	655	34	2749	25	2739	26	1912	29	1474	31	1476	32	2024	26	1382	33	2796	24	2593	27	256	55	64	71	137	54	1575	25	769	33	944	46	1469	33	1377	41	2274	30	1480	34	1770	32	2116	32	1017	37	428	50	2066	27	342	51	717	40	2382	21	1197	36	1340	29	1764	29	631	61	846	40	587	59	555	58	10043	13	7569	14	8324	14	765	38	1346	35	1199	29	2341	25	2508	25	1043	44	939	42	993	34	0	.	263063	2
	broadleaves	2564	21	1521	26	1074	29	2657	26	279	45	1200	33	3983	19	3953	16	6555	13	4159	15	978	36	1172	32	1837	25	0	.	566	52	1581	31	1725	25	1893	29	504	56	1747	23	964	36	728	28	603	34	1851	23	485	40	5559	14	2344	31	1333	30	1781	26	632	38	458	34	319	36	354	36	520	30	1082	35	75	40	1815	29	1317	30	1460	35	1448	27	1772	29	2417	24	2686	20	1623	29	1340	26	1599	29	1812	25	1059	32	348	65	1322	35	1583	29	1543	35	1424	25	175	47	755	31	367	53	1475	24	2337	23	3357	17	1779	23	1715	25	2492	23	1009	36	95	81	286	53	1243	34	742	43	605	39	758	44	251	51	1405	27	380	60	227	44	368	32	941	30	1356	30	969	39	947	35	552	52	297	50	544	58	460	32	394	29	1688	21	532	43	1188	33	716	32	1478	25	1393	26	1241	32	1019	37	967	29	0	.	124102	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	.	0	.	0	0
	total	3462	19	2241	23	2133	26	5700	19	3158	29	5641	22	21007	10	16932	10	17920	10	10833	12	1046	35	1615	31	2743	25	177	.	1662	34	4812	21	5363	19	4671	20	614	53	6159	19	8558	14	5255	17	7480	16	14576	11	13504	10	12284	12	5290	21	6759	18	7893	18	1656	32	2911	24	1505	33	1512	32	3653	22	2756	27	1835	31	4620	21	3385	25	2846	26	4492	21	4442	23	6688	19	5169	18	3113	24	2368	25	2166	28	2467	24	3809	22	3086	24	3234	25	3056	25	3018	28	3448	21	1557	30	3551	21	2960	25	1731	23	2401	23	3493	17	3354	18	2485	22	3435	21	2479	28	1472	39	2559	28	2723	27	2512	27	2721	30	1776	36	679	42	3471	22	722	51	944	36	2750	21	2138	27	2696	24	2732	25	1578	36	1397	37	883	55	1099	45	10503	12	7963	14	10012	13	1297	33	2534	29	1915	26	3819	22	3900	21	2284	29	1958	30	1960	27	0	.	387165	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. 2293373. Birmensdorf, Swiss Federal Research Institute WSL

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