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
grid: 1.4-km grid
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	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	.	80	60	0	.	0	.	0	.	0	0	94	68	0	.	0	.	0	.	0	.	0	.	0	.	3	100	17	79	0	.	102	74	0	.	0	.	10	100	56	51	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	12	100	5	100	135	100	8	100	24	78	0	.	3	100	0	.	12	100	0	.	0	.	0	.	0	.	192	50	0	.	177	100	41	98	15	100	6	100	4	100	10	68	9	100	41	60	4	100	0	.	182	46	7	100	4	100	37	100	172	84	9	71	0	.	0	.	0	.	0	.	18	100	23	100	0	.	0	.	0	.	18	76	0	.	0	.	0	.	0	.	0	.	0	.	53	70	75	51	219	67	0	.	87	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1982	18
	private	32	100	18	100	0	.	89	100	0	.	55	100	57	50	212	73	73	70	0	.	0	.	12	100	63	79	0	.	28	79	0	.	30	69	55	80	0	.	91	82	0	.	0	.	0	.	44	71	0	.	0	.	82	72	12	100	199	57	8	100	0	.	0	.	0	.	0	.	109	84	0	.	0	.	146	61	0	.	2	100	17	100	62	87	26	100	59	79	0	.	0	.	0	.	69	64	82	63	0	.	17	100	0	.	25	71	0	.	0	.	47	71	0	.	17	100	62	100	6	100	5	100	38	100	20	100	35	100	102	100	0	.	0	.	0	.	0	.	0	.	66	90	0	.	3	.	0	.	0	.	0	.	0	.	15	95	0	.	0	.	5	100	10	94	87	73	6	73	0	.	74	100	38	100	0	.	0	.	0	.	0	.	0	.	0	.	2407	15
	total	32	100	18	100	0	.	89	100	80	60	55	100	57	50	212	73	73	70	94	68	0	.	12	100	63	79	0	.	28	79	0	.	33	63	72	64	0	.	193	55	0	.	0	.	10	100	100	42	0	.	0	.	82	72	12	100	215	53	8	100	0	.	0	.	0	.	0	.	121	77	5	100	135	100	154	58	24	78	2	100	19	87	62	87	38	75	59	79	0	.	0	.	0	.	261	40	82	63	177	100	58	75	15	100	31	61	4	100	10	68	56	62	41	60	21	84	62	100	188	45	12	72	42	90	57	74	208	72	111	92	0	.	0	.	0	.	0	.	18	100	89	71	0	.	3	100	0	.	18	76	0	.	0	.	15	95	0	.	0	.	5	100	63	61	162	46	225	65	0	.	161	60	38	100	0	.	0	.	0	.	0	.	0	.	0	.	4389	12
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	575	25	833	32	948	26	962	26	638	42	643	43	3462	16	1651	22	1937	19	1616	19	81	58	180	43	486	35	0	.	289	52	1034	33	1433	26	734	39	64	76	2272	19	3582	16	4747	13	5473	13	10588	8	9301	9	320	38	777	29	1021	34	813	29	40	100	12	100	0	.	18	100	41	100	639	36	1797	22	1895	19	79	66	791	29	2715	18	700	37	748	40	1088	22	1203	24	719	27	419	35	506	29	1756	23	697	32	1096	30	60	50	185	46	343	46	1042	22	2626	16	2037	19	170	35	149	76	29	80	1573	16	697	26	872	27	775	33	443	49	1431	24	509	43	1411	25	1060	30	351	46	194	60	839	29	73	61	229	57	980	24	601	42	670	29	739	31	307	57	325	48	200	78	119	60	7843	9	4658	13	6614	11	648	41	410	42	492	33	229	55	580	32	367	49	126	78	591	31	0	.	117016	2
	private	284	40	20	72	228	75	50	59	1106	31	1667	27	11532	9	6434	13	3109	17	914	32	137	98	9	100	72	50	0	.	196	71	1029	30	1230	33	279	45	20	81	218	52	1815	21	751	39	1742	22	1536	24	385	40	154	73	1497	26	1474	23	4156	16	0	.	129	81	0	.	0	.	75	100	276	47	106	.	481	57	652	35	496	51	343	37	518	46	2013	27	260	44	36	79	214	68	33	80	330	47	654	47	770	39	352	45	607	36	423	42	536	35	113	55	0	.	87	88	0	.	9	100	32	71	0	.	0	.	16	100	292	40	382	53	111	76	524	45	346	40	2083	25	242	50	96	61	42	71	223	71	0	.	233	40	76	69	91	91	93	90	14	93	207	63	0	.	79	75	2592	18	1292	23	920	26	584	44	341	56	460	46	935	26	887	31	245	50	233	48	179	54	0	.	65406	4
	total	859	21	853	31	1176	26	1012	25	1744	25	2310	23	14994	8	8085	11	5046	13	2530	16	218	65	190	41	558	31	0	.	485	42	2064	22	2662	20	1013	31	84	61	2490	18	5397	13	5498	13	7215	11	12124	8	9685	9	474	35	2274	20	2495	19	4969	14	40	100	140	75	0	.	18	100	116	74	916	29	1904	21	2377	19	731	32	1287	27	3058	17	1218	29	2760	22	1348	19	1239	24	933	26	452	33	837	25	2409	21	1466	25	1448	25	667	33	608	32	879	28	1155	21	2626	16	2124	19	170	35	158	72	61	53	1573	16	697	26	888	26	1066	27	824	36	1542	23	1034	31	1757	21	3142	19	593	34	290	45	881	28	295	55	229	57	1213	21	677	38	761	27	833	29	321	54	532	38	200	78	198	47	10435	8	5950	11	7534	10	1233	30	751	34	951	28	1164	24	1467	23	612	36	360	41	771	27	0	.	182422	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	.	27	81	216	60	274	55	0	.	99	100	708	48	455	51	574	36	819	33	151	71	426	54	76	55	102	.	21	100	144	55	317	45	377	51	0	.	1067	35	1104	29	69	72	35	100	669	45	1367	19	442	34	0	.	35	100	0	.	259	51	8	100	70	71	4	100	44	90	42	85	203	65	292	40	97	85	181	80	291	38	483	46	342	50	57	100	0	.	0	.	92	73	101	44	288	51	861	31	256	65	0	.	96	100	0	.	1	100	99	80	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	67	99	145	100	543	43	64	71	13	100	76	71	0	.	25	100	0	.	48	73	59	100	160	74	0	.	42	100	0	.	0	.	172	71	0	.	59	72	414	38	337	56	192	55	229	58	77	100	12	61	81	56	105	64	193	67	18	82	0	.	16934	7
	private	0	.	9	100	0	.	50	100	0	.	526	75	915	28	1978	23	1461	27	330	49	21	100	22	100	22	100	0	.	0	.	0	.	300	52	41	100	0	.	79	94	23	100	10	100	0	.	0	.	0	.	157	64	240	64	239	70	9	100	0	.	58	100	74	100	0	.	0	.	100	100	0	.	113	100	60	71	0	.	157	77	106	77	1474	30	0	.	0	.	0	.	0	.	0	.	0	.	124	91	0	.	141	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	77	0	.	109	71	0	.	103	100	26	100	0	.	31	100	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	71	64	15	100	210	65	44	71	0	.	136	100	66	100	281	58	45	100	0	.	80	100	0	.	0	.	10277	10
	total	0	.	36	65	216	60	324	49	0	.	626	65	1623	26	2433	21	2035	22	1150	27	172	63	448	52	98	48	102	.	21	100	144	55	617	34	418	47	0	.	1146	33	1126	29	78	64	35	100	669	45	1367	19	599	30	240	64	274	62	9	100	259	51	66	89	144	62	4	100	44	90	142	75	203	65	405	40	157	59	181	80	448	37	589	40	1817	26	57	100	0	.	0	.	92	73	101	44	288	51	986	29	256	65	141	62	96	100	0	.	1	100	99	80	0	.	62	100	0	.	0	.	0	.	0	.	0	.	0	.	217	61	145	100	652	37	64	71	116	90	102	59	0	.	56	71	0	.	48	73	59	100	160	74	0	.	114	73	0	.	0	.	172	71	71	64	73	61	624	33	381	50	192	55	365	52	144	71	293	56	126	51	105	64	273	56	18	82	0	.	27211	6
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	35	100	169	58	145	63	1002	31	121	100	377	56	151	77	1546	25	843	30	1498	24	8	100	0	.	0	.	0	.	348	49	290	60	693	41	712	41	0	.	45	92	0	.	0	.	0	.	0	.	0	.	3803	15	137	100	163	82	0	.	431	43	589	34	611	36	650	32	1070	25	0	.	0	.	0	.	317	50	160	100	99	91	149	78	295	100	327	51	73	71	236	63	0	.	0	.	0	.	154	73	101	85	113	90	117	74	314	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	71	0	.	0	.	177	73	0	.	841	36	164	49	471	34	654	27	459	42	474	33	523	39	26	100	314	53	137	72	298	56	0	.	43	100	272	49	181	67	0	.	8	93	72	99	0	.	0	.	165	71	191	65	0	.	23389	6
	private	153	95	14	100	0	.	0	.	229	78	583	45	142	87	412	52	475	45	481	58	0	.	0	.	67	100	0	.	0	.	155	100	238	73	424	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1679	20	0	.	905	34	100	100	128	74	1127	25	172	79	214	55	1756	26	0	.	0	.	1	100	862	41	0	.	62	100	46	100	184	72	0	.	0	.	22	100	0	.	77	59	0	.	0	.	0	.	541	52	168	90	166	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	245	64	98	71	0	.	321	46	197	72	19	100	182	78	0	.	0	.	0	.	127	100	0	.	0	.	4	100	0	.	0	.	82	59	320	51	352	54	108	100	19	100	171	100	0	.	13866	8
	total	188	79	184	54	145	63	1002	31	351	61	960	35	293	58	1958	23	1318	25	1979	23	8	100	0	.	67	100	0	.	348	49	445	52	931	35	1136	32	0	.	45	92	0	.	0	.	0	.	0	.	0	.	5483	12	137	100	1068	31	100	100	559	37	1716	20	783	33	864	28	2826	19	0	.	0	.	1	100	1179	32	160	100	161	68	196	64	479	67	327	51	73	71	258	58	0	.	77	59	0	.	154	73	101	85	654	45	284	61	480	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	71	0	.	0	.	217	62	0	.	1087	32	262	40	471	34	975	23	657	36	493	32	704	35	26	100	314	53	137	72	425	49	0	.	43	100	276	48	181	67	0	.	90	54	392	46	352	54	108	100	184	64	363	58	0	.	37255	5
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	1774	18	1204	24	959	21	1346	20	147	47	217	46	900	22	828	21	3073	13	2868	12	592	32	920	25	1494	23	0	.	517	45	418	35	915	26	861	33	129	53	1040	20	376	27	549	24	343	24	1189	17	139	35	3772	12	510	31	290	45	79	37	448	29	146	34	361	34	243	34	176	28	385	38	194	45	961	26	392	38	636	30	858	23	1061	29	401	36	1891	17	1271	21	979	21	1059	23	1179	21	748	25	190	68	772	31	305	38	450	32	926	33	194	35	894	21	249	32	1296	20	691	25	913	22	1733	15	926	19	1199	19	166	37	75	55	129	48	489	35	400	41	454	33	553	30	98	40	671	26	738	37	285	34	226	28	447	25	947	27	575	28	308	42	189	50	199	43	364	45	266	28	244	28	1105	17	279	36	754	27	775	34	253	39	699	27	486	30	397	37	616	28	0	.	64763	2
	private	1311	26	481	37	145	69	799	40	288	39	1027	30	3099	18	2357	15	2343	15	726	25	255	58	366	54	732	37	0	.	292	57	732	36	628	34	995	28	255	58	661	33	319	48	260	45	292	39	335	35	219	48	1704	19	1351	29	737	25	1298	22	191	64	248	42	62	66	152	68	264	34	411	38	0	.	345	58	951	26	617	44	533	32	588	31	1668	20	356	40	393	47	280	48	159	74	906	30	468	39	200	35	283	48	881	27	511	41	592	30	90	58	63	.	173	57	319	40	1531	22	1953	15	752	33	648	34	940	25	418	44	59	84	73	61	311	42	226	48	329	29	442	46	70	66	529	39	220	48	38	.	84	59	180	46	413	44	358	44	368	44	313	55	0	.	339	49	315	36	231	31	692	26	335	42	223	63	346	40	1106	23	590	27	404	49	377	43	242	48	0	.	51163	3
	total	3085	15	1685	20	1105	21	2144	19	435	30	1244	26	3998	15	3185	12	5416	9	3594	11	847	28	1286	23	2226	19	0	.	809	35	1150	26	1543	21	1857	21	384	42	1702	18	695	26	809	21	634	22	1523	15	358	32	5476	10	1861	23	1026	22	1377	21	639	28	394	29	423	30	396	34	439	23	796	27	194	45	1306	24	1344	21	1253	26	1391	19	1649	22	2069	18	2247	15	1665	20	1258	20	1218	22	2085	17	1216	21	389	38	1055	26	1186	22	961	26	1518	23	284	30	957	21	422	30	1615	17	2221	17	2866	12	2486	15	1574	18	2139	15	584	33	134	48	201	38	800	27	626	31	783	23	995	26	168	36	1200	23	958	30	323	32	310	26	627	22	1360	23	933	24	676	30	502	39	199	43	703	33	580	24	475	21	1797	14	614	28	977	25	1121	27	1359	20	1289	19	891	27	774	28	858	24	0	.	115926	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	2384	17	2233	20	2268	18	3584	15	986	33	1336	32	5220	14	4480	14	6428	11	6895	11	832	29	1526	25	2055	21	102	.	1175	29	1886	24	3361	18	2702	21	194	54	4525	15	5062	13	5365	13	5861	12	12502	8	10807	8	8337	10	1424	24	1509	28	908	28	1178	26	754	31	1042	27	915	29	1330	23	1079	28	2199	20	3284	16	893	32	1792	23	3964	15	2396	22	1786	28	3374	15	2548	19	1933	20	1570	21	1786	20	2984	18	1901	21	2402	22	519	36	862	29	1588	27	1241	20	3629	14	2295	18	1570	17	843	25	942	22	3489	11	1631	18	2076	17	978	30	757	36	1714	23	1565	24	1875	22	1527	26	1157	28	311	48	2399	20	974	34	1034	25	1920	17	1685	23	2092	19	1879	20	641	38	828	31	708	40	781	34	8221	9	5434	12	8549	10	1300	28	1481	24	1353	27	566	36	1360	24	958	28	882	31	1417	23	0	.	224084	1
	private	1779	24	542	35	373	53	988	36	1623	28	3858	21	15745	8	11393	10	7460	11	2452	21	413	50	409	51	957	34	0	.	516	44	1916	26	2425	24	1794	26	275	57	1049	30	2156	20	1020	35	2034	20	1914	21	604	31	3694	16	3170	20	3366	17	5761	14	327	51	1562	22	307	64	366	48	2095	24	896	31	106	.	939	40	2671	21	1113	35	1096	27	1274	29	5400	17	642	37	488	44	517	50	191	71	1313	27	1191	34	1175	30	635	37	2187	22	1102	33	1320	25	203	50	63	.	307	43	319	40	1557	22	2047	15	758	33	653	34	994	24	730	36	626	41	285	53	944	33	572	37	2515	23	750	42	165	58	912	39	541	43	41	.	638	31	453	46	523	48	705	38	396	44	519	51	0	.	621	44	2932	17	1819	19	1666	20	919	36	773	39	992	31	2642	19	1875	23	757	38	710	36	593	45	0	.	143119	2
	total	4163	14	2775	18	2641	17	4572	14	2609	21	5194	17	20965	7	15873	8	13888	8	9347	9	1244	26	1935	23	3012	18	102	.	1691	24	3802	17	5786	14	4496	16	469	40	5575	13	7218	11	6385	12	7895	10	14417	7	11410	8	12031	8	4593	15	4876	15	6669	13	1505	23	2316	18	1349	25	1281	24	3425	17	1975	20	2305	20	4223	16	3564	17	2905	19	5060	13	3671	18	7187	14	4016	14	3036	17	2450	19	1761	20	3099	16	4174	16	3077	17	3037	19	2706	19	1964	23	2908	18	1444	18	3692	13	2602	16	1888	16	2400	16	2989	12	4247	11	2284	16	3070	13	1708	23	1383	27	1999	21	2508	19	2446	19	4041	17	1907	24	476	37	3312	18	1515	27	1075	25	2557	15	2138	20	2614	18	2585	18	1037	29	1348	27	708	40	1402	27	11152	8	7253	10	10215	9	2219	22	2254	21	2344	20	3208	17	3235	16	1715	23	1591	23	2009	21	0	.	367203	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. 2290849. Birmensdorf, Swiss Federal Research Institute WSL

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