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)
development stage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		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
development stage	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	34	.	0	.	0	.	0	.	405	43	68	60	38	72	0	.	0	0	0	.	0	.	0	.	0	.	73	.	63	66	57	99	0	.	69	95	289	59	87	89	20	69	367	46	144	67	67	100	7	.	71	100	45	86	0	.	0	.	0	.	0	.	0	.	0	0	47	98	42	72	37	.	0	.	0	0	0	.	39	100	0	.	0	.	0	.	0	.	0	.	21	.	95	69	33	.	1	100	79	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	35	.	0	.	0	.	10	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	2	100	-2	.	97	60	14	100	0	.	0	.	53	100	13	.	0	.	0	.	0	.	0	.	2609	15
	broadleaves	0	.	29	100	0	.	0	.	0	.	0	.	1	100	1	100	26	62	0	.	0	.	0	.	0	.	0	.	11	.	7	.	4	100	4	100	0	.	13	92	0	.	10	100	9	100	11	100	0	.	8	100	16	.	0	.	25	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	26	84	6	.	0	.	0	.	0	.	0	.	2	.	1	100	0	.	17	97	2	72	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	.	0	.	15	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	2	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	39	.	0	.	2	.	0	.	0	.	0	.	0	.	389	25
	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	.	29	100	34	.	0	.	0	.	0	.	406	43	69	59	64	54	0	.	0	0	0	.	0	.	0	.	11	.	80	.	67	68	61	92	0	.	82	95	289	59	97	81	28	69	378	45	144	67	75	100	23	.	71	100	71	66	0	.	0	.	0	.	0	.	0	.	0	0	47	98	54	67	37	.	0	.	0	0	0	.	65	75	6	.	0	.	0	.	0	.	0	.	23	.	96	69	33	.	18	92	80	99	0	.	6	100	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	77	.	0	.	0	.	0	.	100	.	0	.	15	.	10	100	6	100	0	.	0	.	3	.	0	.	0	.	0	.	2	.	0	.	0	0	5	100	-2	.	97	60	14	100	0	.	39	.	53	100	14	.	0	.	0	.	0	.	0	.	2998	14
young growth/ thicket	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	20	97	59	57	0	0	66	100	0	.	286	100	324	53	180	47	165	67	152	85	26	100	16	73	15	100	0	.	0	0	67	100	45	70	50	74	0	.	117	68	300	45	45	67	223	45	386	37	105	49	136	73	98	99	0	.	48	103	0	.	0	.	0	.	0	.	0	.	5	74	34	.	20	100	48	100	80	83	107	76	0	.	1	100	13	84	55	76	31	100	0	.	0	.	0	.	54	82	0	.	0	.	7	.	0	.	0	.	30	100	37	62	10	100	4	100	26	.	14	71	0	.	1	100	138	64	0	.	18	75	53	79	13	100	2	100	1	100	0	0	0	.	0	.	29	75	0	.	0	.	97	76	79	72	0	.	0	.	0	.	0	.	213	41	70	51	265	63	1	.	0	.	143	70	45	100	47	100	25	80	43	78	28	74	0	.	4816	12
	broadleaves	309	43	98	45	1	100	15	100	0	.	7	100	133	55	61	43	122	38	60	67	121	67	3	100	76	97	0	.	77	75	21	67	55	62	23	86	0	.	112	59	3	77	0	.	8	126	13	63	1	100	69	47	121	72	2	100	4	73	23	.	0	.	0	.	0	.	0	.	11	73	0	.	8	100	14	95	20	95	8	77	0	.	1	100	101	65	49	100	29	100	26	100	26	100	0	.	12	100	0	.	0	.	28	.	0	.	0	.	13	95	118	42	30	70	29	100	9	.	6	61	0	.	0	.	0	.	0	100	2	100	1	100	1	100	0	.	9	100	32	100	0	.	0	.	2	100	0	.	0	.	35	71	53	62	0	.	0	.	0	.	0	.	62	75	6	75	22	56	9	.	0	.	50	68	10	86	95	66	31	55	3	100	84	70	0	.	2584	11
	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	328	43	157	37	1	100	81	100	0	.	293	97	457	41	241	41	288	44	212	65	148	58	19	71	91	98	0	.	77	75	87	79	100	52	73	77	0	.	230	53	304	45	45	67	231	44	399	36	106	50	205	61	220	60	2	100	52	100	23	.	0	.	0	.	0	.	0	.	16	55	34	.	28	100	62	99	100	85	115	72	0	.	2	100	114	62	104	62	60	100	26	100	26	100	0	.	66	72	0	.	0	.	35	.	0	.	0	.	43	75	156	36	41	58	33	89	35	.	20	53	0	.	1	100	138	64	0	100	20	67	54	78	14	96	2	100	10	89	32	100	0	.	0	.	31	73	0	.	0	.	133	74	132	66	0	.	0	.	0	.	0	.	275	36	76	50	286	59	10	.	0	.	193	62	55	97	142	62	55	52	46	79	112	70	0	.	7400	9
pole timber	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	83	62	219	45	291	41	89	60	20	80	164	59	842	36	144	50	722	25	178	63	0	.	17	81	11	100	0	.	198	61	383	42	132	53	357	43	0	.	99	48	318	40	371	39	374	34	960	22	602	30	364	38	262	42	319	41	327	45	18	79	160	65	4	100	2	100	37	.	100	74	63	91	103	73	157	52	241	57	51	57	18	100	126	63	212	49	134	51	106	68	87	84	49	81	66	44	254	50	6	100	392	49	127	51	507	44	38	49	121	46	14	100	9	141	0	.	0	.	247	41	146	67	75	59	78	86	6	82	25	79	137	52	37	60	29	84	100	69	62	62	163	49	25	72	40	71	0	.	76	77	139	74	86	100	68	100	19	100	0	.	53	85	610	28	594	35	555	28	195	64	141	60	258	50	389	47	149	72	169	57	11	79	255	53	0	.	15989	6
	broadleaves	136	49	144	45	162	40	321	60	46	75	4	100	199	64	84	53	372	26	88	65	0	.	4	82	82	75	0	.	70	56	47	66	82	46	105	45	3	100	80	49	75	53	9	86	154	41	587	27	155	56	244	34	50	52	146	52	20	64	21	133	21	77	10	100	8	79	0	.	14	73	13	71	17	79	157	42	172	48	13	119	48	87	31	92	104	55	50	54	82	88	83	52	74	70	185	50	91	57	43	90	261	44	112	48	112	40	220	37	218	34	49	55	819	53	471	34	378	30	828	24	303	36	517	29	60	71	121	57	6	77	135	67	122	58	76	42	68	70	42	87	100	54	17	100	15	74	0	.	1	100	1	100	30	90	81	68	9	91	0	.	2	100	111	37	271	30	532	25	31	67	2	100	137	53	112	58	41	76	117	47	28	88	131	49	0	.	11395	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	218	42	363	37	452	35	410	50	67	62	169	59	1041	33	228	46	1095	20	266	50	0	.	21	81	93	72	0	.	269	51	429	40	214	43	462	39	3	100	179	41	393	34	380	39	528	28	1547	19	756	26	608	34	312	41	466	36	347	44	39	71	181	59	14	100	10	71	37	.	114	66	76	85	120	66	314	41	413	42	65	52	66	73	157	65	316	38	184	51	187	61	170	50	123	74	252	43	346	43	49	81	653	36	239	44	619	38	258	36	339	29	62	48	828	53	471	34	378	30	1076	21	449	36	592	27	138	75	128	55	31	69	272	46	160	51	105	39	168	53	104	66	263	48	43	80	55	63	0	.	77	76	140	74	117	97	149	60	28	77	0	.	55	82	721	25	865	27	1087	21	227	63	143	59	396	46	502	40	190	59	287	43	39	84	386	42	0	.	27384	5
young timber	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	139	65	260	61	278	61	344	46	102	80	269	76	700	30	589	33	1101	31	987	24	75	74	78	63	86	69	0	.	31	.	745	40	295	65	139	80	66	81	985	50	541	33	521	34	340	46	1249	27	510	30	1179	27	257	57	437	42	417	54	237	53	103	77	50	.	2	.	0	.	216	49	49	.	168	100	652	35	12	100	467	41	83	90	490	43	562	40	114	65	113	57	119	60	211	54	163	70	165	63	276	59	192	55	218	62	129	65	20	100	198	58	82	100	21	79	18	100	81	100	239	47	95	60	148	83	154	50	75	71	11	.	121	76	35	83	317	47	56	68	17	.	162	60	16	.	131	80	622	30	59	78	99	69	142	71	12	.	225	78	29	.	70	.	1184	23	850	26	1847	23	202	66	113	57	87	60	14	100	174	63	187	60	0	.	283	52	0	.	25707	6
	broadleaves	137	56	265	48	26	73	185	42	26	78	57	94	144	42	317	41	845	28	1409	20	173	75	288	55	351	45	0	.	2	.	405	52	276	53	385	53	179	55	772	67	74	51	136	56	112	50	541	28	101	54	1233	26	94	63	254	60	20	79	66	73	108	74	19	.	20	.	0	.	285	57	20	.	103	100	656	38	132	72	182	50	312	58	538	38	687	32	197	44	283	42	164	47	534	41	60	68	40	75	94	61	110	62	40	60	79	59	33	100	97	64	43	100	573	32	335	43	1162	22	183	57	725	32	823	27	185	62	1	100	54	.	164	70	80	80	61	72	82	70	3	.	38	84	0	.	206	55	77	62	217	47	412	42	83	84	91	.	0	.	38	.	0	.	177	47	100	52	409	37	57	81	242	45	137	57	94	69	55	75	27	64	67	.	81	53	0	.	20745	6
	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	276	48	525	40	304	60	530	37	128	80	326	72	844	29	906	29	1947	23	2395	19	248	61	366	47	437	47	0	.	33	.	1149	32	571	44	524	48	245	52	1757	41	615	30	657	32	453	38	1789	21	611	27	2413	21	351	47	691	39	437	53	303	47	211	63	68	.	22	.	0	.	501	40	69	.	270	73	1308	30	145	71	649	36	395	51	1028	36	1249	28	310	43	396	39	283	43	744	38	223	64	205	58	370	52	302	54	258	57	208	52	53	73	295	46	125	74	594	32	353	44	1243	22	422	39	820	30	971	27	338	50	76	71	65	.	285	58	115	81	378	48	139	54	19	.	200	56	16	.	336	51	699	30	276	44	511	39	225	58	103	.	225	78	67	.	70	.	1361	22	951	25	2256	21	259	60	355	47	224	55	108	64	229	56	214	59	67	.	364	51	0	.	46453	4
medium timber	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	277	39	232	68	147	50	668	40	686	42	840	36	4261	17	2377	21	1859	27	3008	18	215	72	150	47	827	38	0	.	388	50	704	41	1479	28	843	39	8	100	664	37	1030	30	1533	25	1772	24	2885	19	4164	14	3227	19	481	58	580	43	1555	28	0	.	586	31	410	45	16	.	127	64	457	42	278	71	291	43	731	41	526	41	706	33	289	41	1215	34	918	30	201	53	654	31	259	52	614	26	446	42	990	37	391	52	640	52	224	62	458	39	18	.	555	38	543	43	0	.	0	.	9	100	310	45	220	45	209	59	256	56	264	81	905	37	525	53	494	52	957	34	203	59	75	74	341	42	471	50	338	44	1065	26	156	75	799	31	196	50	239	73	530	58	72	.	228	59	1924	21	2056	21	2296	20	168	56	397	53	508	44	688	35	534	38	179	61	583	43	322	77	0	.	67926	4
	broadleaves	1042	26	297	41	250	44	446	38	108	68	176	46	1596	29	542	25	1670	22	1589	18	170	59	559	39	1334	27	0	.	198	78	164	50	474	52	745	35	165	60	802	32	104	67	209	52	3	81	171	56	2	100	2667	16	583	66	115	49	356	42	144	62	114	41	95	72	43	.	59	81	496	36	0	.	142	54	464	44	564	46	467	33	584	39	1079	38	1001	23	563	42	850	25	695	34	1640	21	121	55	36	75	307	53	143	57	187	63	573	36	0	.	67	54	44	83	101	71	266	54	423	41	42	84	288	35	357	39	64	92	96	100	20	65	372	51	194	66	435	36	169	62	9	100	644	32	458	46	145	45	119	35	162	48	595	31	230	59	683	33	315	60	44	.	695	58	48	93	20	64	322	40	182	42	262	49	124	46	523	33	394	43	119	70	330	46	137	75	0	.	36400	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1320	25	529	40	397	44	1114	31	794	39	1017	37	5858	16	2919	20	3529	19	4597	16	385	54	709	37	2162	25	0	.	586	43	868	38	1953	27	1588	28	173	60	1467	29	1134	28	1742	24	1774	24	3056	18	4167	14	5894	14	1063	45	695	42	1911	26	144	62	701	30	505	42	60	.	185	58	953	33	278	71	433	40	1195	33	1090	32	1174	27	873	34	2294	29	1920	22	764	38	1504	23	954	32	2254	20	567	40	1026	36	699	39	783	48	411	51	1031	34	18	.	622	36	587	41	101	71	266	54	432	41	353	44	509	34	566	37	320	49	360	71	925	37	897	39	689	43	1392	32	372	47	85	71	985	30	929	39	483	41	1184	25	318	54	1393	28	426	43	922	33	845	46	115	.	923	47	1972	21	2075	21	2618	19	350	40	659	43	632	42	1211	31	928	34	299	59	913	38	460	61	0	.	104326	3
old timber	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	626	35	417	41	405	58	1557	27	1607	29	2823	24	5767	18	8948	13	6443	14	1130	36	34	100	367	54	1	100	0	.	818	40	514	49	898	38	1220	32	5	.	1525	31	3892	19	1595	30	3203	19	5619	15	3912	16	2408	22	1522	30	2947	21	2661	25	164	74	1152	29	579	45	630	40	976	42	322	53	548	52	1672	28	1028	38	465	42	1775	28	1626	27	2956	24	344	46	1112	32	363	64	115	71	243	63	2009	23	1494	30	1142	35	560	45	576	44	539	37	660	34	754	35	686	33	70	67	87	100	16	100	447	37	234	51	438	56	238	65	521	48	563	46	957	35	829	35	586	46	1076	43	80	.	708	40	237	59	471	58	212	55	916	31	199	61	969	29	55	.	562	51	178	81	532	52	5179	14	1524	31	1974	24	1032	35	744	40	469	53	644	41	1327	28	627	42	330	60	579	52	0	.	112967	3
	broadleaves	1517	25	731	31	386	42	1263	29	234	43	804	36	1579	28	1711	25	2750	16	396	35	506	46	457	42	501	53	0	.	336	59	437	48	303	46	796	40	139	.	219	41	336	50	174	48	45	50	234	51	3	72	1578	20	682	33	837	36	891	31	63	78	164	40	350	45	122	56	80	55	59	93	0	.	922	31	307	55	599	43	568	38	821	31	399	31	517	38	970	30	100	61	315	52	253	62	773	30	202	72	590	38	569	41	584	41	799	30	10	87	233	51	17	71	128	63	293	72	198	48	363	47	138	60	432	43	163	72	5	100	46	65	151	58	230	68	61	75	665	38	3	.	242	46	550	45	2	302	50	62	436	34	288	67	473	33	14	.	317	44	81	68	345	47	33	69	19	71	99	58	458	39	475	41	657	40	495	39	867	27	509	42	442	41	290	49	0	.	40250	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2143	23	1148	29	792	38	2820	21	1842	28	3627	22	7346	16	10659	12	9193	12	1526	31	540	45	824	39	502	53	0	.	1154	35	952	41	1201	33	2017	29	145	.	1743	29	4228	18	1770	29	3248	19	5853	14	3914	16	3986	17	2205	25	3784	20	3552	23	227	61	1316	29	930	37	752	38	1056	41	381	48	548	52	2594	23	1335	33	1064	34	2342	25	2448	24	3354	24	862	37	2082	27	463	64	431	45	495	55	2783	21	1697	28	1732	31	1130	35	1160	38	1337	28	670	34	987	32	704	33	198	53	380	60	215	47	810	32	372	45	870	36	401	53	527	47	609	46	1108	34	1059	33	647	46	1741	33	83	.	951	35	786	40	473	58	262	54	1352	27	486	52	1442	23	68	.	879	44	259	64	878	40	5212	14	1542	31	2073	24	1491	31	1219	33	1125	35	1138	32	2194	24	1136	34	771	40	869	42	0	.	153217	3
mixed	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	137	53	0	.	421	50	235	51	0	.	189	47	5991	12	3095	17	1411	29	1063	29	1	100	0	.	116	47	87	.	0	.	268	43	855	32	287	49	0	.	594	35	738	28	1664	21	1221	23	3359	15	3054	14	306	38	507	41	187	.	1156	26	353	43	345	55	98	72	367	39	2381	19	0	.	1120	29	498	35	147	71	52	79	696	36	0	.	762	40	25	.	90	61	41	.	18	.	0	.	0	.	105	73	224	64	224	67	73	77	0	.	440	34	1071	28	735	32	124	54	26	72	20	100	630	27	183	53	110	53	121	66	256	49	343	45	156	44	364	41	1447	31	56	100	0	.	406	44	105	100	108	73	487	33	84	72	345	44	30	71	8	.	0	.	257	63	5	.	1817	22	2591	17	1700	20	102	.	0	.	64	100	311	44	32	66	44	74	0	.	31	100	0	.	49170	4
	broadleaves	501	71	236	70	384	42	251	47	0	.	275	58	821	29	911	23	882	29	373	32	160	61	0	.	110	51	0	.	0	.	77	75	425	42	172	61	0	.	233	48	227	51	141	53	203	47	317	33	121	57	295	40	316	47	1	.	277	37	210	41	45	71	21	80	90	48	408	25	0	.	137	61	132	54	63	69	42	81	166	44	0	.	244	51	26	.	131	54	0	.	55	.	0	.	0	.	29	100	60	58	302	51	147	74	0	.	35	68	354	37	165	68	330	31	939	25	687	31	1015	26	158	51	156	49	62	71	15	79	26	100	248	48	80	64	100	53	22	81	7	.	164	48	116	63	11	100	70	70	74	83	105	48	32	100	30	.	0	.	79	78	94	.	156	44	28	41	432	28	0	.	0	.	132	75	185	47	19	121	147	64	0	.	20	73	0	.	16311	6
	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	639	66	236	70	805	34	486	40	0	.	464	50	6812	12	4006	15	2293	23	1435	25	160	61	0	.	226	48	87	.	0	.	344	42	1281	28	459	44	0	.	828	33	964	28	1805	20	1423	21	3676	14	3174	13	601	37	823	38	188	.	1433	25	563	38	390	52	119	73	457	36	2788	19	0	.	1258	27	630	32	210	60	93	71	861	31	0	.	1006	40	51	.	221	52	41	.	73	.	0	.	0	.	134	76	284	54	526	44	220	61	0	.	475	33	1425	24	900	29	454	28	965	25	707	31	1646	19	341	40	267	38	183	66	271	47	368	44	404	38	443	40	1547	30	78	94	7	.	570	38	222	63	118	74	557	32	158	62	450	38	62	77	38	.	0	.	336	66	99	.	1973	21	2619	17	2132	18	102	.	0	.	196	71	495	41	51	72	191	59	0	.	51	87	0	.	65481	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	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. 2300487. Birmensdorf, Swiss Federal Research Institute WSL

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