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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NFI3
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
grid: 1.4-km grid
state 2004/06
		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	56	60	57	36	35	58	40	32	28	69	34	48	240	12	212	14	189	19	128	20	6	64	15	52	14	30	0	.	9	46	73	34	59	29	49	29	0	.	41	22	97	22	82	27	173	20	337	19	308	15	132	17	88	29	83	22	99	22	5	102	49	30	12	46	32	52	63	34	28	40	39	29	90	38	20	86	29	40	52	26	5	278	26	70	45	41	43	35	17	51	12	73	20	32	70	40	36	42	22	35	33	60	31	42	96	44	18	31	49	27	32	35	4	50	0	100	0	.	56	26	33	59	16	49	16	89	8	40	41	30	40	42	23	41	58	31	27	40	8	58	41	29	0	3569	20	43	40	30	34	51	26	34	37	39	16	56	12	53	5	89	23	73	239	15	181	18	191	22	46	30	28	47	11	40	22	35	38	75	26	75	19	55	28	41	0	.	5069	4
	broadleaves	63	23	50	27	29	55	66	31	16	47	20	42	259	28	91	18	123	16	139	21	20	36	41	33	52	23	0	.	18	38	33	56	35	25	51	34	29	53	64	28	43	37	21	28	89	32	190	23	34	43	225	17	40	30	75	43	77	27	18	53	30	29	13	46	16	39	21	36	22	37	15	42	63	35	33	35	48	33	50	35	57	38	41	30	82	27	39	29	35	30	27	31	29	42	53	34	24	31	29	35	65	32	37	47	99	52	69	42	23	104	58	34	152	24	128	23	239	22	186	17	140	21	178	20	18	35	35	52	5	115	15	124	54	32	61	29	23	32	10	60	37	26	26	55	28	48	21	35	21	57	25	42	30	31	36	43	7	51	2	100	16	73	99	28	71	23	204	18	12	46	12	44	50	54	32	26	38	51	31	39	25	58	35	43	0	.	5196	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	.	0	0
	total	119	31	106	24	64	42	106	28	44	49	54	35	499	16	302	12	312	14	266	16	26	33	56	30	66	20	0	.	27	30	106	30	94	22	101	25	29	53	105	21	140	20	103	22	262	18	527	15	343	14	357	13	128	24	158	26	176	18	23	48	80	24	26	36	48	37	84	27	50	29	54	28	152	27	54	41	77	29	102	22	62	43	67	34	127	23	82	27	52	28	39	36	49	31	123	29	59	29	51	30	98	31	68	32	195	34	87	35	72	41	90	25	155	24	128	23	239	22	242	15	174	22	194	19	34	55	43	44	46	31	55	48	77	29	119	22	50	28	18	54	77	24	27	80	48	36	61	26	55	40	51	29	67	29	52	38	20	48	7	73	38	54	338	13	253	15	395	14	58	27	40	35	61	46	54	26	76	45	57	41	44	41	64	30	0	.	10264	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	84	41	184	34	155	33	105	29	100	28	192	28	895	12	646	16	598	18	321	28	36	65	13	100	62	36	0	.	71	46	189	32	287	23	204	35	4	100	277	30	397	18	403	18	436	16	895	12	987	11	460	15	202	25	310	23	384	20	17	117	114	23	81	46	62	40	187	29	139	41	219	26	180	32	147	32	166	39	175	28	57	47	112	29	240	35	120	35	120	35	69	61	80	31	107	31	258	27	80	37	164	32	161	35	272	33	67	32	166	23	112	26	19	73	4	100	5	100	113	36	117	49	32	68	88	33	58	37	169	29	123	33	51	38	169	29	49	79	16	63	99	36	48	38	50	33	182	22	91	39	137	45	73	39	65	72	53	44	20	64	56	60	823	14	812	14	786	15	129	40	93	54	67	40	149	60	97	37	104	47	27	50	167	40	0	.	17710	3
	broadleaves	312	21	173	26	121	26	235	28	57	52	99	31	485	16	390	16	606	14	514	15	84	43	102	33	158	30	0	.	73	32	151	28	167	33	226	27	58	46	270	39	102	34	82	29	209	26	462	16	140	38	523	15	173	28	127	29	213	25	39	51	67	36	27	53	31	38	81	26	139	39	48	52	196	26	114	34	174	26	131	29	170	36	239	25	235	22	80	33	165	35	125	32	151	32	160	34	99	40	94	40	168	32	106	38	113	31	87	31	171	25	127	30	404	21	404	20	625	16	537	17	488	24	544	18	95	35	71	49	16	74	145	36	112	32	124	26	84	43	30	75	136	29	105	49	82	49	72	33	114	36	95	29	100	28	127	41	50	52	13	58	39	42	150	28	243	23	464	15	60	35	64	33	91	41	87	28	120	30	122	33	153	40	55	44	0	.	15868	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	396	20	357	24	276	23	341	22	157	26	291	22	1380	10	1037	13	1204	12	836	15	120	37	115	32	220	26	0	.	144	32	340	23	455	20	429	22	62	45	547	25	499	17	485	16	645	14	1357	10	1127	11	983	12	375	21	437	19	596	16	57	49	180	22	108	39	93	32	268	23	278	29	267	26	376	23	261	25	340	25	306	22	226	31	351	23	475	21	200	26	285	27	193	30	231	25	267	25	357	24	174	32	332	25	268	29	385	26	154	24	337	18	238	20	422	20	408	19	630	15	650	16	605	22	576	17	183	30	129	32	185	27	268	27	163	27	293	24	133	40	46	54	235	26	153	40	133	35	254	21	204	27	232	30	173	28	192	38	102	40	33	49	94	41	973	12	1055	12	1250	12	189	32	158	35	159	30	236	40	217	24	226	28	180	36	222	34	0	.	33579	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	279	32	209	38	509	30	253	39	605	28	585	27	3150	11	1902	13	1921	16	1078	17	41	68	91	43	133	29	32	.	294	40	664	27	810	25	456	29	52	90	782	37	1164	17	1238	17	1047	16	2785	11	2574	10	1451	15	568	24	657	23	1182	20	135	46	388	32	202	39	137	38	377	26	264	33	501	27	538	27	449	25	270	35	728	22	239	29	620	21	399	29	205	32	208	34	104	49	243	29	371	33	518	25	212	39	490	33	274	35	223	59	114	36	489	25	342	30	60	49	10	100	65	100	352	22	142	41	186	40	291	31	300	38	431	30	242	27	204	32	568	27	210	40	36	64	319	28	190	44	130	39	602	21	102	34	223	28	273	34	53	69	224	44	31	60	104	49	1983	12	1828	13	2016	13	218	36	201	33	300	43	420	28	456	26	210	41	156	58	264	36	0	.	48955	2
	broadleaves	638	21	232	30	286	26	606	28	102	40	422	26	1115	18	1093	16	1482	13	1468	14	167	41	360	31	633	20	0	.	116	43	278	31	526	28	589	27	187	46	533	26	228	33	175	36	111	34	591	20	135	38	1537	13	286	27	403	29	483	26	81	36	191	38	127	45	110	48	208	29	390	36	37	58	364	27	489	27	485	29	425	25	464	26	647	23	779	22	267	32	410	26	270	30	828	23	318	27	97	43	165	29	412	29	210	33	366	24	92	48	208	24	110	39	541	22	676	20	954	16	672	20	507	24	645	20	196	38	32	64	53	59	345	32	181	37	243	29	279	32	34	48	467	26	93	51	173	46	106	51	346	25	369	28	233	39	246	34	80	40	57	59	249	52	190	30	93	35	553	19	151	33	175	35	188	30	378	26	331	27	218	33	129	49	96	35	0	.	33609	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	917	20	441	24	795	22	859	23	707	26	1007	22	4265	9	2995	11	3403	11	2546	12	208	37	452	28	766	19	32	.	410	32	942	21	1336	19	1045	20	239	43	1315	25	1391	15	1414	16	1158	15	3376	10	2709	10	2987	10	853	19	1059	20	1664	17	216	34	579	25	329	31	247	35	585	21	654	28	538	25	902	20	939	20	755	22	1153	17	703	21	1267	18	1178	19	471	24	618	21	374	26	1072	20	689	23	614	23	378	28	901	24	484	27	589	27	206	32	698	20	452	24	601	21	686	20	1019	17	1024	16	648	21	831	18	487	29	332	36	484	28	587	22	386	26	811	23	489	27	70	47	786	21	283	38	303	33	709	20	448	22	593	21	506	26	299	31	304	37	88	53	353	40	2173	11	1921	13	2569	12	369	27	376	27	488	31	799	22	787	21	429	28	285	39	360	29	0	.	82564	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	497	25	424	40	432	32	1031	22	939	25	1778	20	6787	9	5235	10	3512	12	2559	13	164	53	154	38	515	33	38	.	486	33	1058	25	1558	19	1148	24	8	100	1332	20	2648	15	1747	14	2254	14	5172	9	4933	9	2827	13	887	22	1750	19	2478	17	254	35	881	20	476	31	241	36	1065	21	294	29	601	24	950	21	1291	22	463	28	1412	20	613	30	1586	19	878	24	445	28	489	28	223	38	552	23	877	22	1286	21	650	23	792	30	450	32	391	27	225	36	700	21	701	24	103	43	17	82	41	61	666	22	239	32	257	36	280	32	365	39	612	27	719	32	523	31	1130	21	464	40	100	56	580	25	341	44	426	31	1081	18	370	29	522	23	629	27	164	80	510	45	169	52	315	39	3236	10	2678	12	2978	11	566	28	478	37	536	25	768	23	918	22	300	30	433	39	410	37	0	.	97056	2
	broadleaves	1432	19	729	22	442	30	804	22	183	37	459	35	1241	17	1228	17	2251	12	1443	14	331	34	523	30	855	21	0	.	340	43	461	31	569	28	737	25	165	52	937	27	222	39	252	39	86	44	339	19	47	48	2869	13	864	37	298	28	466	29	281	33	127	34	227	39	109	43	197	29	251	36	55	62	405	31	575	26	661	32	502	23	777	26	959	28	857	19	709	22	563	25	623	26	979	20	466	28	126	62	379	29	413	25	319	33	503	25	31	49	211	25	96	48	263	34	597	21	694	24	360	27	367	23	453	22	124	58	66	76	46	56	367	35	129	56	282	33	302	32	37	79	430	25	362	32	97	39	102	35	284	32	743	25	369	32	456	31	258	36	111	52	384	35	94	34	37	59	301	25	353	30	331	31	376	26	466	23	538	24	351	30	360	33	283	31	0	.	43146	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	1929	17	1153	21	874	22	1835	16	1122	24	2237	18	8028	8	6463	9	5763	9	4002	11	495	30	677	27	1370	20	38	.	826	27	1519	20	2128	16	1885	19	173	51	2269	17	2870	14	1999	14	2341	14	5511	9	4981	9	5695	10	1751	22	2048	17	2944	15	535	26	1008	20	703	27	350	30	1262	19	545	24	656	23	1355	18	1865	19	1124	23	1915	17	1390	22	2545	17	1735	18	1154	20	1052	21	846	24	1531	18	1343	18	1411	20	1028	20	1205	23	769	26	894	21	257	32	911	17	797	22	365	27	614	20	735	23	1025	17	605	22	710	20	404	28	431	37	657	26	1085	25	652	27	1413	21	765	29	137	46	1010	20	703	29	523	30	1183	17	654	24	1264	20	999	21	620	33	767	36	280	43	699	28	3330	10	2715	12	3279	11	919	24	809	27	912	21	1234	20	1456	19	651	25	793	29	693	28	0	.	140203	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	366	30	314	33	446	36	1531	22	744	29	1981	24	7219	11	7405	10	5519	11	2431	17	104	70	355	46	333	40	18	.	575	40	768	28	1053	24	1097	25	15	100	1620	22	2803	16	2346	19	3241	14	5634	10	3688	12	2819	17	1391	27	1744	20	2066	18	360	38	914	25	369	40	546	35	1828	19	374	35	780	29	1037	24	892	28	448	33	1435	22	1102	27	3245	19	512	28	893	31	474	48	191	47	221	44	1281	25	1061	25	1109	32	531	35	386	42	651	30	758	27	1324	21	910	23	49	61	103	71	42	73	700	23	347	32	490	45	310	40	469	42	611	37	826	26	971	27	1448	21	742	37	75	76	751	31	281	45	491	45	481	24	694	31	771	25	492	32	85	69	538	41	310	50	391	51	4648	12	2183	15	2762	15	757	37	594	33	615	29	786	26	767	28	591	36	333	36	629	37	0	.	110394	2
	broadleaves	1197	20	615	24	331	28	770	28	56	52	324	32	1373	26	827	27	2207	16	350	24	527	34	284	38	757	32	0	.	147	56	234	41	321	40	626	36	47	90	428	32	224	42	149	41	39	50	293	30	26	77	940	17	500	32	452	48	356	36	107	49	38	59	102	62	17	100	40	58	62	54	16	77	307	42	449	30	161	38	296	43	298	39	431	27	491	30	865	27	171	44	294	35	539	31	144	41	67	100	427	39	344	43	428	45	482	31	18	77	368	35	46	67	622	70	547	37	345	29	684	31	111	41	466	29	100	66	34	100	34	78	199	42	231	55	87	42	328	36	0	.	119	46	554	39	0	.	15	87	128	56	204	44	168	36	32	62	247	55	59	61	449	45	55	44	0	.	295	33	161	46	398	37	572	36	454	30	445	32	228	52	203	54	273	35	0	.	30254	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	.	0	0
	total	1563	19	930	21	777	25	2300	18	800	28	2305	23	8592	10	8232	10	7726	10	2781	16	631	31	639	33	1090	26	18	.	722	34	1002	25	1374	21	1723	22	61	73	2049	19	3027	15	2495	19	3280	14	5927	10	3714	12	3760	14	1890	22	2196	20	2422	17	467	31	952	25	471	34	563	34	1868	19	436	32	796	28	1344	22	1341	22	609	27	1731	20	1400	24	3676	17	1003	22	1758	24	645	43	484	29	760	29	1425	23	1128	24	1536	27	875	29	814	35	1133	24	776	26	1693	18	957	22	671	65	650	33	388	28	1385	19	458	27	955	27	410	35	503	40	646	37	1025	25	1202	25	1535	21	1071	31	75	76	870	28	836	32	491	45	496	24	822	27	975	23	660	27	117	53	785	35	369	48	841	35	4703	12	2183	15	3057	14	918	32	992	28	1187	25	1240	21	1213	24	819	31	536	31	902	29	0	.	140648	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	1281	21	1188	25	1576	24	2960	18	2416	23	4572	18	18290	8	15400	9	11740	10	6518	12	351	48	628	34	1056	30	87	.	1435	28	2753	19	3767	17	2954	19	79	68	4052	19	7108	12	5817	13	7152	11	14824	8	12490	8	7688	11	3134	19	4542	16	6208	14	771	30	2347	19	1141	29	1018	28	3520	17	1100	26	2139	22	2794	20	2800	20	1376	24	3802	16	2016	23	5588	16	2073	19	1706	23	1308	25	599	31	1116	22	2706	19	3158	19	2073	24	2010	24	1303	26	1633	22	1183	23	2728	17	2097	19	234	36	135	67	153	59	1888	16	878	24	981	31	985	27	1200	30	1865	24	1950	23	1772	23	3374	19	1493	33	234	45	1790	21	860	34	1117	30	2386	16	1291	24	1678	20	1504	22	382	51	1336	34	536	43	889	36	10929	9	7682	11	8733	10	1715	25	1395	27	1530	25	2145	20	2276	20	1232	26	967	33	1499	29	0	.	279184	1
	broadleaves	3643	16	1799	19	1209	22	2481	19	414	31	1323	26	4473	16	3629	14	6669	10	3914	11	1130	27	1311	25	2455	20	0	.	694	37	1156	27	1619	22	2230	21	487	40	2232	27	819	27	679	26	534	24	1875	15	383	32	6094	10	1863	26	1355	26	1594	21	527	28	453	26	496	35	284	33	546	22	864	29	171	51	1335	24	1660	22	1529	25	1404	20	1766	22	2317	21	2443	15	1960	20	1343	19	1338	23	2527	17	1141	23	412	40	1094	26	1401	23	1100	27	1563	20	298	30	982	21	437	31	1981	25	2352	17	2857	13	2439	15	1612	18	2285	16	533	34	238	50	154	44	1071	26	707	32	797	24	1015	28	112	47	1188	21	1141	29	381	34	316	27	893	23	1436	22	901	25	898	28	642	37	241	42	1136	39	588	23	444	22	1817	15	737	27	980	26	1277	24	1418	20	1472	20	950	27	869	28	742	27	0	.	128074	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	4924	15	2987	17	2785	18	5441	14	2830	22	5895	17	22763	8	19029	8	18408	8	10431	10	1481	25	1938	23	3512	18	87	.	2129	23	3909	17	5386	15	5184	16	565	39	6284	16	7927	11	6496	12	7686	11	16699	7	12873	8	13783	8	4997	17	5898	15	7803	13	1298	23	2800	18	1636	25	1301	26	4067	17	1964	21	2310	21	4129	17	4460	17	2905	19	5206	14	3782	18	7906	15	4516	14	3666	18	2651	19	1936	21	3643	16	3847	17	3570	18	3167	20	3411	19	2403	23	3196	18	1481	20	3710	14	2534	17	2215	23	2487	17	3010	13	4326	12	2490	16	3267	15	1517	24	1438	28	2019	23	3020	19	2479	20	4171	17	2508	25	346	38	2979	17	2001	25	1498	26	2702	15	2184	20	3114	17	2405	18	1280	27	1979	29	777	39	2025	28	11517	8	8126	10	10550	9	2452	22	2375	22	2806	20	3563	17	3749	17	2182	22	1837	26	2241	24	0	.	407258	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. 2293371. Birmensdorf, Swiss Federal Research Institute WSL

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