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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	0	.	0	.	0	.	0	.	335	56	85	76	28	100	0	.	0	.	0	.	0	.	0	.	0	.	133	.	81	81	117	99	0	.	126	96	0	.	0	.	0	.	362	60	32	.	118	100	13	.	130	.	71	93	0	.	0	.	0	.	0	.	0	.	0	.	83	100	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	191	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	-4	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2113	22
	broadleaves	0	.	51	100	0	.	0	.	0	.	0	.	2	100	2	100	18	78	0	.	0	.	0	.	0	.	0	.	0	.	13	.	8	100	8	100	0	.	24	92	0	.	18	.	0	.	21	100	0	.	14	100	30	.	0	.	51	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	11	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	518	30
	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	.	51	100	0	.	0	.	0	.	0	.	337	56	87	74	46	77	0	.	0	.	0	.	0	.	0	.	0	.	146	.	89	82	125	93	0	.	150	95	0	.	18	.	0	.	383	58	32	.	132	100	42	.	130	.	121	67	0	.	0	.	0	.	0	.	0	.	0	.	83	100	0	.	0	.	0	.	0	0	0	.	38	100	11	.	0	.	0	.	0	.	0	.	0	.	192	71	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	.	0	.	0	.	0	.	164	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	4	.	0	.	0	0	0	0	-4	.	0	.	24	100	0	.	65	.	0	.	0	.	0	.	0	.	0	.	0	.	2631	20
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	36	100	71	79	0	.	117	.	0	.	2	100	495	62	172	73	146	95	30	94	47	100	10	100	26	.	0	.	0	.	0	.	45	96	32	100	0	.	201	73	189	75	76	67	216	64	120	97	89	71	0	.	0	.	0	.	79	103	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	81	30	.	0	.	0	.	23	84	10	.	56	100	0	.	0	.	0	.	7	.	0	.	0	.	12	.	0	.	0	.	52	.	0	.	0	.	0	.	49	.	0	.	0	.	0	.	147	81	0	.	22	.	0	.	0	.	4	.	2	.	0	.	0	.	0	.	16	100	0	.	0	.	53	.	112	88	0	.	0	.	0	.	0	.	330	46	47	62	144	70	1	.	0	.	254	71	0	.	86	100	0	.	60	100	52	74	0	.	3911	16
	broadleaves	528	45	131	55	0	.	27	.	0	.	12	100	87	88	64	57	175	45	106	67	206	71	5	100	127	.	0	.	94	.	5	.	29	77	6	100	0	.	97	94	4	100	0	.	17	100	0	.	0	.	18	100	217	71	3	.	4	100	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	35	94	0	.	1	.	0	.	185	66	0	.	51	100	0	.	47	.	0	.	0	.	0	.	0	.	50	.	0	.	0	.	0	.	51	82	52	76	53	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	57	.	0	.	0	.	4	100	0	.	0	.	29	.	75	76	0	.	0	.	0	.	0	.	21	86	3	71	8	100	14	.	0	.	79	71	3	.	125	78	26	86	5	100	157	70	0	.	3152	15
	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	564	45	202	47	0	.	144	.	0	.	13	100	582	55	236	60	321	54	136	66	253	61	15	100	153	.	0	.	94	.	5	.	75	87	38	100	0	.	298	70	193	74	76	67	233	60	120	97	89	71	18	100	217	71	3	.	82	103	43	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	177	83	30	.	1	.	0	.	208	63	10	.	107	100	0	.	47	.	0	.	7	.	0	.	0	.	62	.	0	.	0	.	52	.	51	82	52	76	53	.	66	.	0	.	0	.	0	.	147	81	0	.	22	.	0	.	1	.	4	.	2	.	57	.	0	.	0	.	19	100	0	.	0	.	82	.	187	83	0	.	0	.	0	.	0	.	351	44	50	62	151	67	16	.	0	.	333	63	3	.	210	71	26	86	65	100	209	71	0	.	7064	12
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	133	70	284	57	146	78	96	87	0	.	185	74	531	40	192	62	858	29	162	96	0	.	22	.	0	.	0	.	105	87	490	48	120	73	324	61	0	.	111	66	239	68	390	55	248	50	907	30	377	48	349	62	322	50	477	46	444	61	30	79	235	77	7	100	4	100	0	.	125	100	97	100	26	.	75	91	257	82	49	65	30	.	187	71	332	55	142	52	101	100	10	100	12	.	39	66	314	67	0	.	429	69	138	63	644	56	5	100	113	47	23	.	23	100	0	.	0	.	373	49	19	100	57	100	20	.	2	.	32	.	114	70	20	87	0	.	119	91	111	62	92	100	19	.	46	94	0	.	129	77	167	.	0	.	128	100	0	.	0	.	0	.	715	39	682	52	307	51	143	86	125	76	324	62	600	52	157	100	115	100	5	100	134	57	0	.	15715	8
	broadleaves	104	82	144	60	97	61	206	82	0	.	8	100	248	92	120	59	277	38	87	98	0	.	5	.	104	.	0	.	44	77	46	90	105	54	70	71	0	.	97	62	10	75	13	100	153	57	634	37	122	69	273	47	50	65	126	58	35	62	38	132	33	89	17	100	13	79	0	.	18	100	23	71	2	.	82	79	174	70	30	95	11	.	53	92	53	77	57	63	47	83	114	67	35	.	247	61	13	100	68	.	226	78	109	69	82	76	107	67	156	48	0	.	661	38	459	49	305	40	570	38	299	58	508	42	54	.	73	.	1	.	125	100	74	74	16	.	42	84	50	121	73	73	0	.	26	74	0	.	2	100	1	.	0	.	152	67	14	.	0	.	0	.	124	47	199	43	409	35	14	100	0	.	136	79	77	78	4	100	139	62	7	100	159	54	0	.	9761	8
	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	237	58	428	49	243	55	302	63	0	.	193	75	779	43	312	54	1135	27	249	73	0	.	27	.	104	.	0	.	150	83	536	48	226	54	393	58	0	.	208	58	249	66	402	54	401	40	1541	25	499	40	623	51	372	49	603	44	479	59	69	72	268	69	24	100	17	71	0	.	143	88	120	92	28	.	157	71	432	63	79	55	41	.	240	71	386	49	199	53	148	94	124	63	47	.	286	56	327	67	68	.	655	53	247	64	726	52	112	65	268	34	23	.	684	37	459	49	305	40	942	33	318	55	565	39	73	.	75	.	33	.	239	71	94	65	16	.	162	77	161	75	165	86	19	.	71	77	0	.	131	76	169	.	0	.	280	60	14	.	0	.	0	.	839	34	881	42	716	33	157	80	125	76	460	60	676	48	160	98	254	61	11	72	293	53	0	.	25476	6
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	208	74	0	100	85	.	3	.	0	.	468	75	633	38	826	39	1362	38	1232	32	2	.	134	68	149	70	0	.	54	.	621	60	48	100	177	100	91	100	1113	67	416	53	615	42	0	.	1080	38	274	37	1073	41	212	83	544	55	684	56	264	71	135	.	0	.	3	.	0	.	283	66	0	.	278	100	617	38	21	.	321	63	6	100	581	57	884	45	178	73	32	100	107	68	169	87	23	.	104	91	113	.	108	78	393	62	187	76	0	.	242	71	0	.	26	100	31	100	0	.	134	75	94	92	36	59	191	57	0	.	19	.	0	.	62	84	213	74	88	73	30	.	264	67	29	.	0	.	721	40	9	100	94	98	110	.	22	.	270	.	54	.	0	.	1294	29	1116	33	754	42	147	.	171	66	101	76	24	100	302	62	75	.	0	.	335	61	0	.	23970	8
	broadleaves	23	104	266	58	17	.	95	.	0	.	102	94	145	58	288	55	1022	32	1706	26	209	.	170	56	455	55	0	.	4	.	672	52	471	53	211	100	264	63	1125	75	38	73	83	58	126	71	475	44	141	69	960	35	161	62	275	86	35	79	1	100	7	.	0	.	35	.	0	.	342	75	0	.	176	100	718	41	92	.	211	65	530	60	842	42	904	40	201	60	366	52	179	60	322	64	19	.	11	86	65	.	61	71	42	74	108	70	0	.	107	100	78	.	114	79	266	63	1436	28	45	98	627	55	995	35	285	66	0	.	89	.	0	.	143	81	64	100	14	100	5	.	12	73	0	.	0	.	50	62	246	61	581	48	27	.	162	.	0	.	70	.	0	.	189	69	54	67	289	58	0	.	276	58	198	67	60	71	96	75	26	.	111	.	98	63	0	.	21581	8
	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	232	71	266	59	102	.	98	.	0	.	570	71	778	38	1114	35	2384	27	2938	24	210	.	303	50	605	57	0	.	58	.	1294	40	519	54	388	71	355	59	2238	51	455	51	699	42	126	71	1555	30	415	37	2033	30	372	58	819	52	719	55	266	71	142	.	0	.	38	.	0	.	625	51	0	.	453	72	1334	37	113	.	532	56	536	60	1424	42	1788	33	378	57	397	51	286	50	491	58	42	.	115	91	178	.	169	73	435	61	295	60	0	.	349	58	78	.	140	67	298	66	1436	28	179	79	722	51	1031	34	476	55	0	.	108	.	0	.	206	82	277	79	101	75	34	.	276	67	29	.	0	.	771	40	255	62	674	46	136	.	183	.	270	.	124	.	0	.	1483	28	1170	32	1043	36	147	.	447	58	299	65	84	72	398	55	101	.	111	.	433	59	0	.	45551	6
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	128	60	370	78	202	58	770	49	602	46	810	46	3902	21	2066	31	2113	32	3324	22	16	75	92	71	867	58	0	.	338	71	913	49	779	45	966	43	14	.	451	53	1305	36	1137	38	1562	34	2490	28	4752	19	2691	26	476	69	856	47	1450	38	0	.	683	42	345	59	0	.	223	63	672	47	218	.	122	100	821	55	336	71	601	41	269	65	638	39	802	38	0	.	637	43	288	65	415	34	256	62	655	61	650	53	426	61	194	75	587	47	33	.	593	46	1016	43	0	.	0	.	17	100	91	.	298	51	288	73	273	82	378	.	1219	44	568	71	762	56	547	56	261	71	87	.	410	56	314	79	363	55	853	32	5	89	639	40	205	73	370	82	386	59	130	.	33	.	1023	40	1835	31	2689	26	102	81	538	62	606	46	636	44	816	41	190	78	511	66	122	92	0	.	64490	5
	broadleaves	965	38	320	54	327	53	477	48	46	70	156	59	1003	29	421	34	2053	27	1389	24	240	71	803	47	579	41	0	.	60	64	179	57	196	61	999	37	134	.	810	40	172	68	119	57	4	100	289	60	5	100	2039	24	936	72	177	54	303	54	182	77	173	46	45	67	0	.	104	81	634	41	0	.	4	73	430	68	750	58	420	36	180	95	1159	57	867	34	399	78	722	33	599	48	1406	31	66	98	5	100	465	58	179	67	300	65	491	44	0	.	76	62	16	94	0	.	58	.	714	44	0	.	447	39	206	62	116	93	0	.	19	85	474	61	137	70	249	59	98	71	0	.	637	41	122	100	211	52	130	50	152	72	438	47	239	83	670	43	466	66	80	.	38	.	2	100	11	100	411	46	165	60	457	49	193	49	575	42	452	47	96	97	417	59	217	78	0	.	33567	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	1093	37	689	50	529	52	1247	39	649	45	966	47	4906	19	2487	29	4167	22	4714	20	256	71	895	47	1446	44	0	.	398	65	1092	45	975	41	1966	31	148	.	1261	40	1478	33	1255	37	1566	34	2779	26	4756	19	4730	20	1412	53	1033	45	1754	34	182	77	856	39	391	58	0	.	327	58	1305	38	218	.	126	99	1251	46	1086	45	1021	34	448	60	1797	46	1669	32	399	78	1360	31	887	45	1821	30	322	58	660	61	1114	41	606	57	494	58	1078	44	33	.	670	43	1031	43	0	.	58	.	730	43	91	.	745	36	495	55	390	65	378	.	1238	44	1041	49	899	50	796	51	360	62	87	.	1047	38	436	71	574	51	983	32	157	71	1076	38	443	59	1040	44	852	52	210	.	71	.	1025	40	1845	31	3100	24	267	60	995	47	799	45	1211	41	1267	37	286	71	928	52	339	71	0	.	98057	4
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	466	51	265	56	307	99	1914	33	2235	33	2604	31	4972	23	7292	17	7011	17	574	44	0	.	194	100	0	.	0	.	636	58	706	57	1363	42	936	44	10	.	1840	39	4095	25	1233	38	3430	25	5831	20	4252	21	2320	27	1477	40	3619	26	3184	31	53	.	1002	42	745	49	664	53	773	53	326	72	269	.	2158	35	762	62	473	54	1404	39	2154	33	2765	34	403	57	1211	43	139	.	49	100	185	81	2029	29	1892	35	1016	47	251	54	976	44	742	42	652	47	354	76	483	60	99	86	0	.	30	.	284	72	239	73	397	83	394	65	424	60	541	58	910	42	454	63	518	61	696	69	0	.	927	40	202	86	457	74	166	81	808	45	75	.	1054	37	100	.	511	80	71	.	524	72	3926	20	1198	40	2325	29	280	67	471	51	0	.	628	55	1147	36	876	48	423	71	325	71	0	.	108181	4
	broadleaves	833	35	500	44	342	64	1818	33	230	52	457	47	1707	36	2095	32	2703	20	351	46	341	71	218	65	279	63	0	.	270	82	358	60	266	64	357	74	248	.	273	52	409	61	298	47	44	71	144	74	5	72	1998	25	543	42	734	44	1159	37	47	.	186	54	229	53	185	65	95	75	8	100	0	.	1231	32	297	89	743	54	608	49	806	42	323	45	552	47	820	43	48	.	443	60	339	77	630	47	334	75	527	54	606	51	937	44	964	37	19	87	163	94	4	85	241	63	0	.	49	.	358	65	56	72	436	58	268	72	10	100	61	82	246	59	281	88	71	100	580	58	0	.	435	46	200	78	10	100	63	76	498	43	95	.	486	44	25	.	103	61	58	.	261	76	3	100	23	93	84	88	343	62	444	61	134	.	587	50	757	38	544	51	498	49	229	70	0	.	38660	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	1299	36	765	44	649	58	3732	25	2465	33	3061	30	6679	20	9387	17	9714	15	925	42	341	71	412	68	279	63	0	.	907	51	1064	49	1629	39	1293	43	258	.	2113	37	4505	23	1531	35	3474	24	5975	19	4257	21	4318	22	2020	33	4353	24	4343	29	100	.	1188	42	974	46	849	49	868	51	334	73	269	.	3388	28	1059	52	1216	44	2013	34	2960	30	3088	33	955	48	2031	36	187	.	492	59	525	78	2659	27	2227	33	1542	41	858	48	1913	39	1706	32	671	47	517	61	487	60	340	58	0	.	79	.	643	48	295	71	833	50	662	53	433	60	602	59	1156	40	735	54	589	63	1276	52	0	.	1363	37	402	71	467	74	229	80	1306	38	170	.	1540	31	125	.	614	76	129	.	785	59	3929	20	1221	40	2409	28	623	59	914	48	134	.	1215	41	1903	31	1421	39	922	49	554	58	0	.	146841	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	161	73	0	.	252	68	258	65	0	.	182	64	4926	18	2864	22	751	51	1172	34	0	.	0	.	174	55	153	.	0	.	239	59	762	50	290	71	0	.	561	49	845	36	1098	33	944	34	2196	26	2889	19	205	65	410	48	309	.	1068	35	467	53	303	71	182	72	424	54	2120	28	0	.	865	39	430	54	140	100	64	.	485	54	0	.	416	47	46	.	130	71	0	.	32	.	0	.	0	.	179	72	139	80	333	74	0	.	0	.	448	50	1260	39	817	43	105	100	29	100	37	100	603	43	105	71	21	100	192	75	287	72	364	55	180	60	416	57	547	69	102	.	0	.	203	66	0	.	0	.	392	43	20	.	247	60	32	.	0	.	0	.	253	.	10	.	1980	32	2573	22	1498	28	175	.	0	.	117	.	345	56	9	.	28	100	0	.	57	.	0	.	42916	5
	broadleaves	703	89	242	.	92	65	75	60	0	.	369	71	919	40	1228	27	652	50	402	38	236	71	0	.	165	60	0	.	0	.	119	79	431	58	49	98	0	.	167	74	176	62	217	59	212	72	98	59	123	73	244	60	97	72	2	.	220	65	138	62	47	100	39	80	55	95	260	36	0	.	78	69	138	68	84	87	16	.	43	89	0	.	204	65	48	.	207	59	0	.	96	.	0	.	0	.	49	100	73	76	341	62	0	.	0	.	46	91	190	55	165	100	211	51	1232	31	552	46	784	36	108	71	143	81	111	71	3	100	47	100	299	54	20	94	38	66	31	.	13	.	179	70	0	.	0	.	100	82	112	.	126	60	0	.	0	.	0	.	105	.	167	.	170	60	37	49	310	43	0	.	0	.	79	.	266	53	36	.	189	79	0	.	23	.	0	.	15048	9
	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	864	85	242	.	344	61	333	63	0	.	551	64	5845	18	4092	19	1403	39	1574	31	236	71	0	.	338	56	153	.	0	.	357	56	1192	41	339	71	0	.	728	45	1022	33	1315	31	1156	31	2294	25	3012	18	449	58	507	50	311	.	1288	35	605	53	350	70	221	73	480	52	2380	27	0	.	943	37	569	47	224	75	80	.	528	50	0	.	620	50	94	.	337	59	0	.	128	.	0	.	0	.	228	75	212	61	674	52	0	.	0	.	495	48	1450	36	982	40	316	48	1261	30	589	47	1388	29	213	50	164	75	303	72	290	72	411	53	479	48	436	55	585	68	134	.	13	.	382	51	0	.	0	.	492	41	132	.	373	50	32	.	0	.	0	.	358	.	177	.	2150	31	2610	22	1808	27	175	.	0	.	196	.	611	50	45	.	217	74	0	.	80	.	0	.	57964	5
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	1134	29	989	37	992	40	3159	24	2837	28	4251	22	15795	11	13498	12	12269	12	6496	14	65	75	452	50	1215	43	153	.	1135	40	3102	25	3198	24	2841	24	115	81	4403	25	7091	17	4548	17	6400	16	12986	12	12665	11	6757	15	2909	26	5936	19	6979	18	814	39	2358	25	1279	34	1095	38	3116	23	1406	32	1531	32	3013	28	2415	29	1294	33	2892	24	2458	29	4588	23	2491	23	1671	33	964	34	486	43	781	32	2347	26	3342	25	1917	32	1548	32	1713	31	2161	26	1139	33	2614	24	2339	27	253	55	60	71	133	53	1485	27	756	34	799	49	1217	33	1220	42	2197	30	1772	32	1715	33	1887	32	1269	43	227	50	1897	26	565	54	881	45	2133	21	971	39	1275	28	1512	29	620	56	1167	46	508	58	567	67	9268	13	7447	14	7716	14	873	38	1304	33	1402	29	2232	26	2516	23	1284	36	1000	45	1026	33	0	.	261296	2
	broadleaves	3157	26	1654	25	875	33	2698	25	276	45	1104	32	4111	19	4216	18	6900	13	4040	15	1231	34	1201	34	1709	25	0	.	472	52	1391	31	1507	27	1700	30	645	50	2594	35	810	37	748	28	555	36	1661	22	395	40	5546	14	2033	36	1316	32	1807	27	449	41	447	31	330	39	289	47	459	31	1001	36	101	55	1550	28	1612	31	1810	34	1312	28	1528	32	2620	29	2620	21	1683	30	1234	26	1431	30	2150	26	962	35	413	62	1198	34	1442	30	1439	32	1645	26	172	49	692	33	313	60	1278	25	2068	23	3072	18	1757	24	1538	27	2289	22	834	36	85	86	217	52	1143	33	657	45	543	38	766	45	124	68	1336	26	322	61	250	45	342	35	1016	30	1269	29	826	36	1009	34	587	54	312	51	465	56	509	35	327	30	1511	21	537	44	1177	33	885	30	1568	26	1468	26	1020	34	1039	35	882	32	0	.	122287	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	4290	23	2644	23	1867	28	5856	19	3114	28	5355	22	19906	10	17714	11	19169	10	10536	12	1296	33	1653	32	2924	27	153	.	1607	34	4493	21	4705	20	4542	20	760	48	6997	22	7901	16	5296	17	6955	15	14647	11	13060	10	12303	12	4942	22	7252	18	8786	17	1264	32	2805	24	1609	32	1384	35	3575	22	2407	27	1632	31	4564	23	4027	24	3103	26	4204	21	3985	24	7207	20	5111	18	3354	25	2199	24	1917	28	2930	25	3309	23	3756	24	3114	26	2991	25	3151	27	3806	22	1310	30	3305	21	2652	25	1531	24	2129	23	3206	17	3243	19	2294	23	3088	21	2051	28	1306	40	2414	28	2914	26	2372	28	2430	30	2035	36	351	46	3233	22	887	48	1132	40	2476	20	1987	28	2544	23	2339	25	1629	33	1754	40	821	55	1032	48	9777	12	7774	14	9227	13	1409	34	2481	28	2286	26	3801	22	3984	20	2304	28	2038	33	1908	27	0	.	383583	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. 2300539. Birmensdorf, Swiss Federal Research Institute WSL

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