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
volume of bolewood (HG 2010)
type of assortment · ownership (2 categories)
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
type of assortment	ownership (2 categories)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	public	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	private	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	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	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
coniferous wood	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	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	76	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	19	81	0	.	0	.	156	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	404	37
	private	59	100	30	100	0	.	147	100	0	.	0	.	0	.	2	100	80	100	0	.	0	.	21	100	84	100	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	167	94	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	103	80	0	.	0	.	0	.	106	72	0	.	0	.	28	100	0	.	26	100	0	.	0	.	1	100	0	.	29	100	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	9	100	0	.	101	100	0	.	0	.	127	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	1296	26
	total	59	100	30	100	0	.	147	100	0	.	0	.	0	.	2	100	80	100	12	100	0	.	21	100	84	100	0	.	0	.	0	.	6	100	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	167	94	9	100	0	.	0	.	13	100	0	.	0	.	15	100	0	.	103	80	0	.	0	.	0	.	145	59	0	.	0	.	104	78	0	.	26	100	0	.	0	.	1	100	0	.	29	100	0	.	57	69	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	9	100	18	100	121	85	0	.	0	.	282	59	67	100	0	.	0	.	0	.	0	.	0	.	0	.	1700	22
coniferous shortwood	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	.
	public	496	36	503	42	674	42	912	36	1096	42	978	48	3835	21	1402	34	2333	21	1992	24	38	77	134	69	596	46	0	.	484	53	846	47	860	39	407	70	79	100	1698	30	4133	19	3446	19	4104	20	9789	12	9474	10	250	47	683	36	1200	43	1191	33	0	.	21	100	0	.	0	.	69	100	918	43	1102	37	1351	28	91	88	586	39	1685	29	644	56	803	55	1467	25	1206	32	679	35	476	48	440	44	1752	33	566	47	1331	35	26	100	215	56	503	51	949	35	2328	25	2128	27	114	65	21	100	54	81	1174	28	603	35	748	42	943	42	442	64	1594	31	252	65	1253	37	585	52	394	63	166	79	744	41	109	72	74	95	723	38	276	64	673	39	770	46	446	68	364	62	0	.	7	100	5876	13	4329	17	6049	16	195	63	273	53	570	42	353	61	549	42	408	66	209	78	545	43	0	.	106853	3
	private	247	58	14	100	0	.	64	73	1022	48	1815	33	10784	12	5523	16	3485	21	1356	36	1	100	16	100	8	100	0	.	155	100	1191	38	1681	40	371	50	36	81	252	66	2087	27	747	45	1803	30	929	42	364	61	0	.	1580	34	1569	30	4362	22	0	.	231	82	0	.	0	.	0	.	245	66	176	.	670	67	181	61	557	57	348	51	655	60	1429	46	266	57	0	.	0	.	57	80	73	60	148	81	642	51	398	60	712	46	458	49	614	42	138	75	0	.	139	100	0	.	0	.	59	70	0	.	0	.	0	.	341	48	478	71	192	80	175	54	349	54	1376	41	291	62	170	61	38	100	0	.	0	.	171	60	7	100	0	.	147	100	21	100	179	100	0	.	0	.	2290	27	1116	29	895	39	184	69	170	71	182	81	899	37	969	38	280	65	98	100	173	72	0	.	60848	5
	total	743	31	517	41	674	42	976	34	2118	31	2794	27	14619	10	6925	15	5819	15	3347	20	39	74	150	62	604	46	0	.	639	47	2037	30	2541	29	778	44	115	73	1949	28	6220	15	4193	18	5907	17	10718	11	9837	10	250	47	2263	26	2769	25	5553	18	0	.	253	75	0	.	0	.	69	100	1163	36	1278	35	2020	29	272	50	1143	34	2034	26	1299	41	2232	35	1733	23	1206	32	679	35	534	43	513	38	1899	31	1208	35	1729	30	737	45	673	38	1117	33	1086	32	2328	25	2267	26	114	65	21	100	113	53	1174	28	603	35	748	42	1283	33	919	48	1786	29	427	44	1602	31	1961	33	685	45	337	50	782	40	109	72	74	95	894	32	284	62	673	39	917	41	467	65	543	53	0	.	7	100	8166	12	5445	15	6944	15	379	46	442	43	752	37	1252	32	1518	28	688	47	307	62	718	37	0	.	167701	2
coniferous mediumwood	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	.
	public	0	.	44	81	0	.	406	62	0	.	0	.	336	100	350	54	544	47	886	35	0	.	289	77	35	79	180	.	0	.	194	64	461	49	641	51	0	.	1750	37	125	71	47	100	0	.	59	100	0	.	724	36	0	.	63	100	0	.	286	59	15	100	55	100	6	100	76	89	0	.	346	65	374	49	146	100	236	100	176	52	759	49	422	58	0	.	0	.	0	.	0	.	106	61	504	51	793	44	38	100	0	.	158	100	0	.	2	100	183	80	0	.	116	100	0	.	0	.	0	.	0	.	0	.	0	.	111	100	266	100	662	54	117	71	27	100	61	100	0	.	43	100	0	.	31	100	104	100	98	100	0	.	77	100	0	.	0	.	314	71	0	.	108	72	527	46	482	64	208	65	259	73	0	.	7	100	52	100	33	100	280	76	34	82	0	.	15834	10
	private	0	.	15	100	0	.	89	100	0	.	297	100	485	48	1947	29	1806	33	47	100	38	100	0	.	39	100	0	.	0	.	0	.	161	100	70	100	0	.	132	94	0	.	16	100	0	.	0	.	0	.	129	77	204	77	404	70	16	100	0	.	0	.	129	100	0	.	0	.	170	100	0	.	207	100	104	71	0	.	258	77	182	77	889	41	0	.	0	.	0	.	0	.	0	.	0	.	193	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	196	100	0	.	169	75	0	.	0	.	43	100	0	.	54	100	0	.	0	.	0	.	0	.	0	.	129	100	0	.	0	.	0	.	94	79	25	100	343	69	73	71	0	.	242	100	110	100	224	56	75	100	0	.	0	.	0	.	0	.	9810	12
	total	0	.	59	65	0	.	496	54	0	.	297	100	821	50	2297	26	2351	28	933	33	38	100	289	77	75	65	180	.	0	.	194	64	622	45	711	47	0	.	1881	35	125	71	63	79	0	.	59	100	0	.	853	33	204	77	467	62	16	100	286	59	15	100	185	76	6	100	76	89	170	100	346	65	581	47	250	65	236	100	434	51	942	42	1311	34	0	.	0	.	0	.	0	.	106	61	504	51	986	41	38	100	6	100	158	100	0	.	2	100	183	80	0	.	116	100	0	.	0	.	0	.	0	.	0	.	0	.	307	73	266	100	831	45	117	71	27	100	104	72	0	.	97	71	0	.	31	100	104	100	98	100	0	.	206	73	0	.	0	.	314	71	94	79	133	61	870	39	555	57	208	65	501	61	110	100	230	55	127	72	33	100	280	76	34	82	0	.	25644	7
coniferous longwood	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	.
	public	65	100	69	74	242	63	1007	42	208	100	652	55	184	100	1836	31	656	43	1399	31	14	100	0	.	0	.	0	.	272	75	399	69	655	61	670	58	0	.	75	92	0	.	0	.	0	.	0	.	0	.	3693	23	234	100	274	81	0	.	399	65	766	44	717	41	708	40	974	35	0	.	0	.	0	.	523	51	0	.	165	100	196	100	0	.	334	71	0	.	203	98	0	.	0	.	0	.	264	73	28	100	189	88	202	75	358	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	71	0	.	0	.	105	100	0	.	997	37	151	75	492	45	797	31	489	55	449	43	549	51	47	100	266	58	155	100	210	100	0	.	0	.	250	74	116	100	0	.	14	100	1	100	0	.	0	.	141	100	165	82	0	.	24066	8
	private	16	100	26	100	0	.	0	.	288	100	46	100	0	.	447	70	758	47	311	62	0	.	0	.	124	100	0	.	0	.	255	100	0	.	343	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1354	29	0	.	1347	36	178	100	234	74	1010	36	76	100	205	72	1498	32	0	.	0	.	1	100	948	54	0	.	113	100	0	.	308	72	0	.	0	.	0	.	0	.	82	73	0	.	0	.	0	.	220	60	278	90	284	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	71	92	.	0	.	237	70	138	100	33	100	6	100	0	.	0	.	0	.	209	100	0	.	0	.	0	.	0	.	0	.	145	59	542	52	547	57	179	100	35	100	0	.	0	.	13018	11
	total	81	83	95	61	242	63	1007	42	496	71	698	52	184	100	2283	28	1414	32	1711	28	14	100	0	.	124	100	0	.	272	75	654	57	655	61	1013	43	0	.	75	92	0	.	0	.	0	.	0	.	0	.	5047	18	234	100	1621	33	178	100	633	49	1776	27	794	38	913	35	2472	23	0	.	0	.	1	100	1471	39	0	.	278	72	196	100	308	72	334	71	0	.	203	98	0	.	82	73	0	.	264	73	28	100	409	52	481	61	642	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	71	0	.	0	.	105	100	0	.	1100	34	243	60	492	45	1034	29	628	48	482	41	556	51	47	100	266	58	155	100	419	71	0	.	0	.	250	74	116	100	0	.	159	54	543	52	547	57	179	100	175	83	165	82	0	.	37084	6
broadleaf roundwood	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	.
	public	1394	24	1058	36	889	30	1111	25	84	54	262	59	876	32	1018	26	3266	17	2847	16	706	40	894	34	1102	27	0	.	335	59	591	41	740	31	327	55	126	67	983	28	378	39	606	30	279	39	1267	24	156	51	3048	17	669	36	442	49	91	43	249	41	191	40	284	38	238	47	196	36	343	57	69	40	1213	31	519	45	501	52	933	31	1267	38	533	43	1921	22	1098	31	1121	27	1038	31	980	29	563	40	257	85	702	41	294	46	430	45	605	40	127	49	701	30	174	55	1043	26	457	38	581	36	1470	23	966	27	945	29	238	43	63	85	155	64	649	42	434	57	397	47	172	57	84	56	926	31	324	69	185	44	298	35	579	31	916	34	434	43	437	48	142	50	272	51	205	88	197	40	119	37	1022	23	226	61	787	36	395	39	140	57	650	38	587	37	617	40	739	32	0	.	59943	3
	private	906	39	391	47	0	.	1049	46	204	58	775	38	2489	24	2608	20	2490	19	660	34	166	96	253	95	389	56	0	.	103	88	745	46	708	42	1108	33	299	79	654	41	422	57	179	64	281	62	223	61	181	57	1665	27	1305	41	677	35	1453	26	354	64	175	51	45	73	99	70	244	49	574	44	0	.	190	63	703	43	1056	44	496	47	363	46	1659	28	352	54	408	63	95	.	278	74	587	46	347	58	76	49	382	58	973	34	764	47	659	34	39	91	0	.	150	100	191	71	1448	26	2291	19	231	76	316	51	1248	31	666	46	3	100	80	82	303	53	279	62	132	57	641	53	114	71	338	51	49	.	0	.	29	60	156	64	242	64	349	64	398	54	346	74	0	.	285	70	341	55	188	39	497	44	251	63	171	65	273	44	1253	28	615	36	448	59	265	76	87	73	0	.	47977	4
	total	2300	21	1449	29	889	30	2160	26	288	44	1037	32	3365	19	3626	16	5756	13	3507	14	872	37	1147	34	1491	25	0	.	438	49	1336	31	1448	26	1435	28	425	59	1638	23	799	35	786	27	560	36	1490	22	337	38	4713	14	1975	30	1119	28	1545	25	603	41	366	31	329	34	337	39	440	31	916	34	69	40	1403	28	1222	31	1557	34	1429	26	1630	31	2191	24	2273	20	1506	28	1216	26	1316	29	1567	25	910	33	333	67	1084	33	1266	28	1194	34	1265	26	166	43	701	30	324	55	1234	24	1905	22	2872	17	1701	23	1282	23	2192	21	904	36	66	81	235	51	952	33	714	42	529	38	812	43	197	47	1265	26	373	61	185	44	326	32	736	28	1158	30	783	37	835	36	488	54	272	51	489	55	538	38	306	28	1519	21	478	44	957	31	668	29	1393	26	1264	26	1035	33	883	36	826	29	0	.	107920	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	.
	public	1954	24	1674	28	1805	26	3436	21	1388	38	1893	36	5231	20	4606	19	6799	14	7137	14	757	39	1317	34	1734	29	180	.	1092	38	2030	30	2722	24	2046	30	205	69	4506	21	4635	17	4099	19	4383	19	11115	11	9630	10	7715	14	1586	28	1978	33	1283	33	935	40	993	38	1057	36	952	36	1315	30	1261	37	1527	31	2938	22	1279	36	1336	34	2959	23	2866	28	1758	35	3722	19	2304	26	2003	26	1514	29	1525	28	2856	25	1880	29	2099	29	584	46	1005	38	1466	33	1078	32	3212	20	2302	26	1273	24	478	37	635	35	2701	18	1570	24	1693	25	1180	38	616	50	2015	30	1603	31	1804	31	1008	44	732	45	250	61	2710	24	584	61	782	36	1921	23	1443	30	2038	25	1830	29	930	45	772	39	741	54	422	70	6198	13	4994	16	7803	14	746	37	1474	32	979	34	500	54	1251	34	1028	37	1246	34	1483	29	0	.	207099	2
	private	1228	34	475	44	0	.	1349	40	1514	42	2933	28	13759	11	10526	13	8619	14	2374	27	206	81	291	91	643	50	0	.	258	74	2191	31	2550	34	1913	27	335	76	1038	38	2509	25	943	40	2084	27	1152	36	545	45	3148	22	3089	26	3998	21	6009	19	602	51	1416	32	250	73	304	71	1742	30	1157	37	176	.	1068	51	1936	34	1613	37	1216	36	1200	45	4301	23	617	47	511	59	95	.	335	72	742	43	601	47	911	44	781	46	1939	29	1500	38	1583	29	177	71	0	.	290	71	191	71	1477	27	2351	19	231	76	316	51	1248	31	1040	40	677	58	272	72	647	47	628	47	1508	41	974	51	284	60	533	46	141	.	0	.	437	47	302	62	276	66	631	50	443	54	525	71	0	.	596	58	2656	26	1748	25	1465	31	435	62	709	53	778	38	2918	23	2206	27	906	46	398	64	260	71	0	.	132950	3
	total	3183	19	2150	24	1805	26	4785	18	2902	28	4826	22	18989	9	15132	10	15419	10	9511	12	963	35	1608	32	2377	25	180	.	1350	34	4222	22	5272	20	3959	20	540	53	5543	19	7144	14	5042	17	6467	15	12267	10	10175	10	10863	12	4676	20	5976	18	7292	17	1536	31	2410	24	1307	32	1256	32	3057	21	2418	26	1704	30	4005	21	3215	25	2949	25	4175	19	4066	24	6059	19	4339	18	2815	24	2098	25	1849	27	2268	23	3458	23	2792	24	2879	25	2523	25	2506	27	3049	22	1255	29	3212	20	2592	24	1464	23	1955	22	2986	16	2932	18	1886	21	2941	19	2220	28	1293	39	2287	28	2250	26	2433	26	2517	30	1706	35	534	43	3244	21	725	52	782	36	2358	20	1745	27	2314	24	2462	25	1374	35	1297	37	741	54	1018	45	8854	12	6742	13	9268	12	1181	33	2183	27	1757	25	3418	21	3456	21	1935	29	1644	30	1742	26	0	.	340049	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. 2290466. Birmensdorf, Swiss Federal Research Institute WSL

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