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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NFI2
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 1993/95
		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	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	37	.	29	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	286	41
	broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	57	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	178	44
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	36	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	.	44	.	29	.	0	.	57	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	24	.	0	.	46	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	463	31
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	0	.	0	.	12	100	0	.	0	.	0	.	259	66	0	.	314	57	0	.	0	.	3	72	19	.	0	.	39	73	0	.	10	100	12	100	0	.	40	97	251	59	0	0	209	61	327	63	120	61	0	.	9	100	158	92	337	66	23	100	14	.	0	.	0	.	0	.	0	.	106	71	29	78	0	.	28	71	11	100	11	100	55	.	0	.	11	80	0	.	0	.	0	.	14	100	92	71	0	.	8	100	0	.	62	100	60	100	45	.	0	.	0	.	0	.	0	.	9	100	30	100	0	.	35	100	57	.	32	71	99	69	61	100	0	.	95	71	0	.	1	100	0	.	11	77	0	.	0	.	0	.	114	.	0	.	0	.	0	.	0	.	9	100	93	66	67	84	7	100	0	.	41	.	0	.	0	.	0	.	0	.	30	100	0	.	3482	15
	broadleaves	0	.	11	100	53	97	1	.	0	.	57	98	2	100	30	100	20	84	4	.	0	.	0	.	102	.	0	.	70	100	0	.	11	100	5	100	0	.	91	71	77	74	0	.	30	78	72	59	0	.	1	100	2	100	0	.	14	62	0	.	-15	.	0	.	0	.	0	.	0	.	7	89	15	87	0	.	45	100	4	80	23	100	25	.	28	100	0	.	14	.	29	.	0	.	13	100	3	100	0	.	3	100	0	.	0	.	7	74	0	.	0	.	26	100	0	.	60	.	34	73	0	.	26	100	0	100	2	.	5	100	0	.	0	.	0	.	16	100	0	.	0	.	0	.	14	100	0	.	0	.	0	.	39	.	0	.	0	.	0	.	0	.	0	.	31	50	6	71	14	72	0	.	0	.	0	.	0	.	5	77	0	.	43	100	0	.	1177	18
	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	.	11	100	65	81	1	.	0	.	57	98	261	65	30	100	334	57	4	.	0	.	3	72	121	.	0	.	108	81	0	.	21	100	17	100	0	.	131	79	329	51	0	0	239	60	399	53	120	61	1	100	11	87	158	92	351	64	23	100	-1	.	0	.	0	.	0	.	0	.	114	67	45	81	0	.	73	83	16	82	34	100	79	.	28	100	11	80	14	.	29	.	0	.	27	100	96	71	0	.	11	78	0	.	62	100	67	92	45	.	0	.	26	100	0	.	60	.	43	76	30	100	26	100	36	99	60	.	37	71	99	69	61	100	0	.	111	71	0	.	1	100	0	.	25	89	0	.	0	.	0	.	154	.	0	.	0	.	0	.	0	.	9	100	124	58	73	81	21	71	0	.	41	.	0	.	0	.	5	77	0	.	73	100	0	.	4658	13
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	196	47	160	70	372	60	34	100	0	.	62	.	771	35	1009	39	1246	34	378	46	0	.	142	95	99	61	0	.	191	72	388	48	281	66	301	62	81	96	25	93	341	48	129	82	90	61	855	31	489	32	1297	36	396	48	438	49	873	40	96	100	144	95	181	71	0	.	113	.	293	53	396	62	132	86	360	55	272	63	167	48	40	89	274	50	613	45	84	79	87	71	99	96	0	.	33	82	488	56	85	100	353	65	392	49	575	59	0	.	287	54	4	100	32	73	0	.	26	100	360	56	90	82	37	67	192	51	124	100	107	.	37	79	16	78	10	.	35	.	65	72	276	72	23	.	37	97	256	53	74	61	102	100	86	.	49	106	252	77	0	.	0	.	1301	23	728	34	435	44	305	60	30	100	334	59	335	55	367	53	101	100	0	.	115	64	0	.	22548	7
	broadleaves	320	45	135	53	57	55	261	71	0	.	3	.	231	54	259	54	931	32	562	39	0	.	92	68	187	60	0	.	30	71	274	62	289	51	19	100	311	63	368	72	77	75	36	83	225	51	859	30	156	52	482	40	35	67	81	46	75	47	47	100	9	100	60	72	0	.	0	.	183	65	34	65	219	70	134	54	143	73	265	63	391	61	273	49	239	51	21	58	160	57	110	78	0	.	119	88	35	67	151	71	271	51	43	80	48	59	40	72	182	48	29	87	686	33	555	36	1363	24	594	41	587	37	823	28	285	48	58	97	6	.	45	100	61	85	10	.	5	.	22	76	225	47	0	.	53	89	21	72	263	57	357	71	13	.	194	60	48	62	0	.	0	.	272	46	78	45	343	38	68	107	50	100	33	77	107	60	86	71	153	71	42	.	66	54	0	.	17134	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	516	39	295	51	429	56	295	64	0	.	65	.	1002	32	1268	34	2177	26	940	34	0	.	234	65	286	55	0	.	220	72	662	42	570	46	320	61	392	58	393	68	417	45	165	81	316	44	1715	22	645	27	1779	30	431	47	519	46	948	39	143	75	153	89	241	66	0	.	113	.	477	42	430	59	351	56	494	53	414	51	432	49	431	57	547	46	852	41	106	73	246	52	208	63	0	.	152	70	523	55	237	75	624	45	435	50	623	57	40	72	469	38	33	77	718	32	555	36	1390	24	954	34	677	34	860	28	477	44	182	76	113	.	82	74	77	70	20	.	40	.	88	72	501	51	23	.	90	92	277	54	338	51	458	60	99	.	243	52	300	74	0	.	0	.	1573	21	806	32	777	32	373	52	80	73	367	59	442	49	454	47	254	61	42	.	181	60	0	.	39682	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	225	38	0	0	27	100	494	58	658	48	1179	44	2752	23	2131	29	2031	28	2107	26	0	.	107	81	65	96	92	.	352	71	1148	47	1992	39	423	80	33	.	1004	62	1131	40	746	42	349	62	1968	29	4132	19	1825	32	675	55	542	49	142	58	90	.	159	70	152	.	99	83	130	65	266	87	632	58	643	41	369	42	0	.	328	62	31	.	782	59	540	50	170	71	357	60	57	88	536	36	323	47	389	73	194	65	0	.	53	.	97	72	0	.	72	.	0	.	0	.	0	.	0	.	262	59	183	71	370	71	31	.	366	.	1068	47	3	293	36	84	257	76	457	53	373	100	340	47	216	.	184	69	523	40	0	.	198	55	365	62	115	63	171	.	74	.	0	.	785	31	937	34	1294	35	269	68	54	100	135	72	242	51	696	45	147	.	431	66	95	.	0	.	45475	6
	broadleaves	1066	32	563	48	308	71	217	53	142	64	238	53	600	34	427	40	1100	31	1700	25	164	.	726	47	180	72	0	.	175	93	251	71	660	46	547	45	124	.	365	61	302	58	63	100	6	100	131	86	16	82	1555	31	243	44	232	74	372	60	0	.	114	75	45	.	48	71	77	87	377	56	0	.	671	46	480	39	734	58	122	63	51	.	361	72	542	42	565	50	527	42	175	77	1406	30	39	92	7	100	423	51	0	.	34	.	102	80	0	.	0	.	0	.	44	.	149	77	547	45	309	60	208	46	255	77	5	.	0	.	57	80	212	73	54	100	83	71	77	64	27	100	609	42	0	.	117	61	69	59	144	68	393	41	302	63	430	53	85	.	31	.	0	.	6	73	27	60	558	39	198	77	235	77	127	73	487	43	260	51	22	.	266	62	7	.	0	.	25772	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	1292	31	563	48	334	72	711	46	801	46	1417	45	3352	22	2558	28	3131	24	3807	22	164	.	833	47	245	71	92	.	526	58	1399	41	2652	34	971	46	157	.	1369	50	1433	34	809	40	355	61	2099	28	4148	19	3380	24	917	46	775	46	514	53	91	.	273	69	197	.	147	77	208	59	643	53	632	58	1314	37	849	38	734	58	450	54	82	.	1143	56	1081	39	735	47	885	43	231	80	1942	29	362	49	396	73	617	44	0	.	87	.	199	75	0	.	72	.	0	.	44	.	149	77	547	45	571	42	391	52	624	58	36	.	366	.	1125	45	215	74	90	75	340	73	534	51	400	93	949	37	216	.	302	64	593	40	144	68	591	40	667	46	546	47	256	.	105	.	0	.	791	31	964	34	1852	29	466	58	289	72	262	71	729	41	956	40	169	.	697	52	102	.	0	.	71248	5
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	453	68	684	60	211	62	2135	30	786	50	1631	36	6227	19	3785	22	5042	21	2567	25	135	97	0	.	898	51	0	.	422	73	903	52	1209	43	2654	31	0	.	912	47	1000	33	1207	32	2632	27	4065	23	3220	21	2427	27	730	54	1719	38	2676	28	79	92	959	38	500	59	132	72	1411	37	475	58	610	51	1619	48	717	62	817	50	629	47	168	.	1113	37	920	36	393	58	469	62	934	54	152	76	757	41	1993	38	677	49	844	47	573	48	313	49	359	67	337	58	791	44	0	.	0	.	0	.	0	.	265	61	0	.	496	71	0	.	515	59	522	67	512	71	542	65	276	67	37	.	632	44	363	52	333	75	461	44	463	51	595	50	664	39	0	.	148	76	182	79	1357	54	1886	27	2021	31	1667	30	165	.	561	49	1641	38	1153	39	959	41	613	52	257	100	690	52	0	.	89048	4
	broadleaves	699	50	169	58	294	59	1079	37	78	72	159	46	1975	28	1255	33	2040	23	952	27	569	47	39	.	822	35	0	.	199	83	108	68	151	59	570	48	0	.	338	53	119	72	131	47	0	.	42	50	0	.	2337	22	873	67	233	48	507	40	288	57	182	52	164	62	58	72	159	43	174	75	98	100	60	90	529	63	750	50	431	53	9	.	1546	44	925	33	463	56	224	60	709	44	223	59	392	65	52	73	196	71	705	41	709	42	610	41	1	100	22	100	8	90	0	.	125	76	138	.	19	.	146	58	292	72	192	87	0	.	-1	100	344	58	136	88	220	66	579	59	70	.	373	55	476	52	64	87	72	63	550	48	385	48	420	63	205	.	102	72	115	73	76	73	18	75	13	100	20	71	52	.	164	55	399	51	568	47	615	49	442	55	207	84	353	58	0	.	33374	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	1152	43	854	57	505	60	3214	27	864	51	1790	36	8202	17	5040	21	7081	17	3519	23	705	49	39	.	1720	36	0	.	621	60	1011	48	1360	42	3224	30	0	.	1250	45	1119	34	1338	32	2632	27	4107	22	3220	21	4764	19	1603	45	1952	36	3183	27	366	50	1141	38	665	52	190	71	1571	36	650	61	708	52	1679	49	1246	46	1567	41	1060	40	176	.	2659	33	1845	31	856	47	693	54	1643	38	375	59	1149	37	2045	38	873	47	1549	37	1282	41	923	40	360	67	358	58	798	44	0	.	125	76	138	.	19	.	411	52	292	72	688	59	0	.	514	59	866	50	648	59	761	54	854	61	107	.	1004	41	839	50	397	72	534	43	1013	42	979	43	1084	37	205	.	250	65	296	76	1433	52	1904	27	2034	31	1687	30	217	.	726	46	2041	35	1720	37	1574	37	1055	45	465	73	1044	46	0	.	122423	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	550	63	1191	48	317	82	1577	38	627	60	667	58	3920	25	4977	22	4610	21	775	46	285	100	196	100	0	.	0	.	264	.	495	50	675	69	231	85	7	.	1655	36	2610	28	1380	36	1860	31	3386	25	2478	25	787	40	1642	42	2797	31	2174	37	435	79	301	59	243	79	571	61	1203	41	0	.	0	.	371	62	792	52	382	.	947	50	1362	40	1723	43	328	71	857	52	147	71	408	72	227	62	562	55	365	71	810	51	544	70	531	54	1033	38	510	51	567	60	319	79	75	100	0	.	22	100	248	71	0	.	115	.	235	60	598	59	195	.	396	50	239	90	191	65	356	100	0	.	359	80	57	.	327	70	96	71	395	75	173	75	347	75	226	.	459	81	223	.	64	71	1891	29	608	53	1879	30	109	.	236	67	116	100	927	48	361	53	515	58	638	64	373	60	0	.	70720	5
	broadleaves	1256	37	686	35	61	71	781	52	9	100	122	100	573	58	1362	40	2743	23	43	59	664	59	178	83	221	71	0	.	8	.	154	55	44	95	239	100	153	.	252	47	287	88	345	40	32	100	98	86	36	80	1041	39	678	44	220	50	409	56	22	94	51	71	65	72	123	91	109	66	0	.	0	.	546	78	39	62	31	.	383	58	477	63	140	71	499	47	308	57	366	63	257	71	359	53	108	62	228	98	298	71	123	90	349	69	730	38	17	90	199	97	0	.	129	85	0	.	205	71	491	54	0	.	0	.	128	73	0	.	45	.	98	100	269	97	0	.	174	100	0	.	48	100	25	.	93	70	-1	567	121	86	82	97	29	100	54	.	145	76	54	.	276	74	0	.	0	.	124	64	75	.	534	54	73	100	446	57	395	52	344	58	410	53	254	67	0	.	23644	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	1806	35	1877	36	377	72	2358	32	636	59	789	60	4493	23	6340	21	7353	17	818	46	949	51	374	72	221	71	0	.	273	.	650	50	719	71	469	72	159	.	1907	34	2896	27	1725	33	1893	30	3484	24	2514	25	1828	33	2321	34	3017	30	2582	36	457	80	352	59	308	75	693	58	1312	39	0	.	0	.	917	55	831	52	413	.	1329	45	1839	40	1864	42	827	47	1165	49	513	59	665	60	586	52	670	55	594	58	1108	47	667	61	879	52	1762	34	527	50	766	52	319	79	204	70	0	.	227	67	738	43	0	.	115	.	363	58	598	59	240	.	494	53	508	71	191	65	530	75	0	.	407	82	83	.	420	70	95	71	516	71	256	71	377	73	280	.	604	75	277	.	340	72	1891	29	608	53	2004	29	184	.	770	51	189	72	1373	42	756	45	859	46	1048	48	626	50	0	.	94364	5
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	0	.	0	.	17	.	0	.	328	82	386	64	3256	23	4197	21	1063	40	756	32	0	.	0	.	33	.	0	.	0	.	730	57	1123	40	377	51	0	.	326	47	924	34	751	36	1139	40	1727	31	804	32	667	44	96	100	839	48	683	53	217	61	336	59	148	79	253	59	318	66	286	60	149	85	355	52	187	.	0	.	758	37	592	54	423	52	0	.	68	.	119	74	20	.	0	.	209	94	424	53	127	78	216	73	0	.	3	.	294	54	781	40	984	38	91	89	43	75	14	100	286	60	115	71	248	93	144	71	93	.	186	59	321	67	438	52	279	53	0	.	0	.	146	72	0	.	0	.	504	45	23	.	82	.	120	73	0	.	0	.	0	.	21	.	2558	27	1832	25	1560	26	149	96	55	.	0	.	206	58	116	55	93	78	0	.	0	.	0	.	37208	6
	broadleaves	0	.	292	.	58	.	126	.	7	433	424	58	246	36	714	30	262	55	481	39	0	.	113	.	42	.	0	.	0	.	303	58	189	46	89	83	0	.	233	55	32	87	56	79	118	99	28	88	79	100	364	42	232	97	236	89	84	70	71	75	33	78	86	71	112	82	15	88	60	100	4	100	92	84	28	.	0	.	217	46	138	58	296	67	0	.	45	.	123	71	43	.	0	.	95	84	46	95	14	100	112	71	0	.	95	.	36	96	96	57	207	93	129	58	985	34	234	48	127	65	27	100	850	42	8	100	0	.	58	100	141	66	111	65	145	76	0	.	0	.	10	100	0	.	0	.	78	77	1	.	1	.	12	100	0	.	207	.	0	.	47	.	33	67	43	72	185	54	58	100	56	.	14	.	260	76	123	56	123	71	0	.	0	.	0	.	10939	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	0	.	292	.	75	.	126	.	335	87	810	57	3502	23	4911	20	1325	37	1237	32	0	.	113	.	76	.	0	.	0	.	1032	54	1312	37	467	54	0	.	560	39	955	34	806	35	1258	37	1755	31	883	30	1032	38	328	75	1074	45	766	54	288	59	369	57	234	74	364	51	333	66	346	61	153	86	448	48	215	.	0	.	975	35	730	47	719	57	0	.	113	.	241	72	62	.	0	.	303	76	470	52	141	74	327	72	0	.	98	.	329	51	877	37	1191	36	220	53	1028	34	249	47	413	47	142	60	1098	40	152	71	93	.	243	64	462	56	549	48	424	55	0	.	0	.	156	71	0	.	0	.	582	43	24	.	83	.	131	71	0	.	207	.	0	.	68	.	2592	26	1875	25	1744	25	207	76	111	.	14	.	466	64	239	56	215	73	0	.	0	.	0	.	48147	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	1424	33	2035	35	956	38	4239	21	2398	28	3925	23	17217	11	16099	11	14305	11	6584	14	420	74	447	56	1113	42	92	.	1268	39	3664	24	5290	22	3997	24	121	70	3963	24	6257	16	4214	18	6342	16	12365	12	11273	11	7003	15	3549	25	6560	18	6885	18	941	42	1913	25	1224	34	1055	38	3175	23	1321	32	1893	29	3150	28	2425	28	1499	39	2840	23	2203	30	4370	23	2400	23	1583	33	1179	33	1517	39	915	29	1898	27	3752	24	1893	29	1965	31	1549	29	2084	26	1223	32	2089	25	2098	27	198	57	43	75	108	54	1164	30	682	34	770	48	1133	36	1238	43	2104	30	1378	33	1303	37	1278	35	1219	39	475	79	1754	27	659	44	892	40	1841	21	955	40	1150	31	1696	27	390	62	1029	45	479	57	1442	51	8430	13	6231	15	6902	14	1003	35	936	35	2266	29	2862	24	2499	23	1469	32	1326	42	1302	34	0	.	268767	2
	broadleaves	3342	20	1856	26	831	35	2466	25	237	47	1004	31	3628	19	4048	18	7133	13	3755	15	1397	36	1149	34	1555	24	0	.	481	50	1089	29	1344	27	1471	30	588	47	1647	26	894	37	630	27	413	41	1238	24	286	41	5780	14	2119	33	1002	31	1460	26	428	42	374	35	420	34	341	45	361	33	794	35	143	70	1604	34	1210	32	1702	34	1422	26	1089	36	2641	29	2233	20	1402	30	1413	26	1323	30	1988	24	779	38	372	63	1082	32	1213	29	1135	34	1584	25	100	47	499	44	269	73	1038	26	1813	23	2548	17	1574	26	968	26	2246	23	618	38	60	93	170	51	840	34	630	48	459	42	851	45	119	64	1265	27	501	49	342	36	239	36	1080	31	1218	29	815	40	883	38	587	45	200	52	399	54	330	38	192	28	1235	22	465	43	1039	34	646	36	1868	25	1480	27	1088	32	925	35	723	37	0	.	112218	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	.
	total	4766	19	3891	23	1787	30	6705	18	2635	28	4929	22	20845	10	20147	10	21437	9	10339	12	1817	34	1596	32	2668	26	92	.	1749	33	4753	21	6634	19	5468	21	709	45	5610	20	7150	15	4844	17	6754	16	13603	11	11559	11	12783	11	5669	21	7562	17	8345	17	1370	34	2286	24	1644	31	1396	34	3536	22	2115	28	2036	29	4753	23	3635	23	3201	28	4262	20	3292	26	7011	20	4633	18	2985	26	2592	25	2840	27	2903	23	2677	24	4123	23	2975	25	3179	25	2684	27	3668	22	1324	30	2588	22	2367	25	1236	24	1857	23	2657	17	2738	20	1650	23	3016	21	1751	29	1299	41	2273	29	2218	27	1933	31	1736	31	2070	34	594	66	3019	23	1160	41	1234	37	2080	21	2034	29	2367	25	2511	24	1273	35	1616	38	678	55	1841	43	8760	12	6423	14	8138	13	1468	31	1975	29	2912	27	4730	21	3979	20	2557	26	2252	31	2025	29	0	.	380985	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. 2300238. Birmensdorf, Swiss Federal Research Institute WSL

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