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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NFI5
growing stock (stemwood)
development stage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	.	194	83	10	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	119	100	0	.	81	85	248	62	197	48	15	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	.	0	.	0	.	40	100	0	.	0	.	11	.	0	.	0	.	0	.	115	.	10	100	0	.	0	.	59	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	114	.	0	.	16	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	73	8	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1421	24
	broadleaves	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	34	.	0	.	0	.	0	.	19	90	0	.	0	.	0	.	0	.	0	0	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	56	.	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	345	35
	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	.	36	.	194	83	10	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	34	.	0	.	0	.	0	.	19	90	119	100	0	.	81	85	248	62	197	48	17	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	.	0	.	0	.	111	88	0	.	0	.	11	.	0	.	0	.	0	.	115	.	25	73	0	.	0	.	59	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	.	170	.	0	.	16	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	73	8	.	10	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	1766	22
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	4	82	23	100	0	.	7	76	0	.	32	80	883	33	366	47	122	74	70	100	3	.	0	.	0	.	0	.	0	.	31	100	230	54	19	.	0	.	20	100	466	47	0	0	446	80	928	38	423	55	66	62	20	100	307	67	35	74	0	.	0	.	44	.	50	94	0	.	0	.	30	81	8	100	31	.	0	0	51	70	0	.	25	.	22	100	0	.	35	72	0	.	0	.	0	.	99	75	67	.	0	.	0	.	66	.	4	77	0	.	0	.	40	94	4	100	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	91	82	0	.	10	.	0	.	0	.	22	100	188	64	61	100	0	.	0	.	22	100	0	.	0	.	10	.	0	.	34	100	86	57	1	100	50	83	38	.	4	.	155	65	0	.	0	.	0	.	0	.	0	.	5891	13
	broadleaves	150	74	33	100	72	.	21	70	0	.	101	78	431	76	77	54	209	59	3	100	42	.	9	.	19	.	0	.	0	.	43	100	48	78	82	.	24	71	26	100	55	76	19	100	15	100	1	100	0	.	32	55	5	100	14	100	163	79	0	.	0	.	8	.	1	100	0	.	0	.	0	.	6	74	22	.	19	100	4	100	14	77	20	.	57	80	0	.	21	72	1	.	0	.	14	.	46	79	0	.	0	.	0	.	51	.	0	0	51	100	0	.	5	53	27	100	0	.	0	.	0	.	0	.	25	100	0	.	6	100	0	.	0	.	0	.	-22	.	0	.	0	.	76	100	25	94	21	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	100	7	79	10	100	28	100	0	.	1	.	31	94	174	72	0	.	16	78	0	.	0	.	2461	19
	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	154	72	57	71	72	.	28	56	0	.	133	72	1314	34	443	42	330	61	73	100	46	.	9	.	19	.	0	.	0	.	73	72	278	54	101	.	24	71	46	100	521	44	19	100	461	77	930	38	423	55	98	56	25	100	321	66	198	68	0	.	0	.	52	.	52	95	0	.	0	.	30	81	14	77	52	.	19	100	55	70	14	77	45	.	80	85	0	.	55	72	1	.	0	.	14	.	146	63	67	.	0	.	0	.	117	.	4	77	51	100	0	.	45	87	31	88	0	.	0	.	0	.	0	.	63	72	0	.	6	100	0	.	91	82	0	.	-12	.	0	.	0	.	99	81	213	62	82	100	0	.	0	.	26	100	0	.	0	.	10	.	0	.	34	100	92	53	11	91	78	71	38	.	6	.	186	61	174	72	0	.	16	78	0	.	0	.	8352	11
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	168	56	150	60	307	62	142	72	0	.	227	101	942	31	247	46	1299	27	82	76	80	70	63	72	163	70	0	.	68	.	2	100	155	69	361	68	0	.	307	72	331	51	139	73	388	32	1148	27	910	33	355	65	325	59	716	47	560	47	208	78	110	78	0	.	29	73	0	.	0	.	86	73	259	71	199	59	244	50	23	94	0	.	83	100	68	76	167	67	11	100	81	71	86	100	167	52	429	51	2	.	193	66	166	73	159	85	56	51	260	51	75	88	0	.	0	.	61	100	438	40	25	100	37	100	79	74	206	90	354	65	100	64	187	69	281	71	271	58	211	75	39	.	49	.	196	71	0	.	0	.	9	.	88	63	90	74	99	90	0	.	73	94	727	32	301	42	529	38	54	70	90	98	331	50	81	64	323	79	137	100	187	71	168	64	0	.	18617	7
	broadleaves	266	38	250	39	56	96	63	59	0	.	39	86	457	42	216	50	452	28	142	50	160	56	137	51	168	80	0	.	17	.	141	99	126	56	123	57	0	.	141	77	19	100	61	96	357	41	442	42	127	57	327	44	229	52	8	59	148	48	162	48	26	81	47	.	21	76	0	.	187	76	26	100	8	78	120	57	61	66	149	78	0	.	64	100	251	54	82	70	75	77	48	94	63	98	277	65	102	71	92	.	171	50	57	67	185	49	137	54	201	49	122	88	816	32	137	58	880	33	582	30	111	56	477	42	47	100	157	59	3	71	67	81	118	59	154	64	126	69	227	76	0	.	2	.	93	53	0	.	42	.	58	.	162	75	90	82	33	76	0	.	40	100	240	41	484	41	512	31	66	71	28	73	136	60	37	65	133	56	291	53	56	71	215	50	0	.	14024	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	433	40	401	39	363	54	205	57	0	.	266	88	1399	27	463	41	1751	23	224	48	241	47	200	53	331	57	0	.	85	.	143	97	281	57	484	54	0	.	448	64	351	50	200	59	746	28	1590	24	1037	30	682	51	554	44	724	46	708	41	370	50	135	73	47	.	50	70	0	.	187	76	112	70	266	69	320	53	304	52	172	70	0	.	146	100	318	52	249	62	86	69	129	58	149	88	444	49	531	51	95	.	364	48	223	59	344	57	193	47	461	37	197	64	816	32	137	58	941	33	1020	28	135	49	515	40	126	82	362	59	356	64	167	65	305	51	434	51	397	56	438	62	39	.	51	.	289	58	0	.	42	.	67	.	250	66	180	61	132	79	0	.	113	72	968	27	785	32	1041	28	120	71	118	91	467	44	119	59	457	59	428	48	242	63	383	47	0	.	32642	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	48	83	410	47	76	71	221	80	177	.	148	96	833	35	739	48	1472	30	248	44	0	.	127	100	97	77	0	.	184	81	750	44	485	59	275	98	0	.	172	68	1045	42	1009	42	574	47	570	52	982	35	742	46	321	72	763	41	196	65	0	.	247	91	0	.	9	.	0	.	382	61	139	83	684	62	54	.	197	72	700	41	44	89	45	.	497	47	46	.	66	100	32	94	22	93	19	.	129	.	0	.	351	75	121	81	367	57	0	.	202	68	74	100	198	75	0	.	0	.	338	49	177	77	313	53	361	58	129	.	62	86	354	56	88	88	22	.	0	.	0	.	263	62	124	75	0	.	69	.	262	68	339	87	195	72	153	80	20	.	0	.	0	.	1329	30	1113	43	983	37	208	.	160	78	0	.	796	47	382	62	0	0	0	.	255	61	0	.	25786	7
	broadleaves	168	82	163	49	55	70	332	72	41	.	136	100	159	53	347	54	875	33	881	35	0	.	84	71	293	61	0	.	92	81	106	68	442	58	156	79	0	.	237	69	108	64	45	70	84	51	702	43	117	100	435	54	59	84	200	51	79	100	0	.	16	100	0	.	8	.	0	.	316	94	11	100	381	54	56	.	428	71	283	63	311	93	13	.	391	51	52	.	303	63	196	59	159	81	69	.	0	.	0	.	24	100	46	74	111	100	0	.	512	51	6	100	187	70	631	45	1276	31	749	44	967	35	549	39	380	58	1	.	88	100	277	62	143	71	32	.	0	.	0	.	239	58	22	95	0	.	12	.	392	52	276	59	4	100	193	69	37	.	0	.	0	.	188	64	33	56	138	48	14	.	74	100	0	.	298	43	316	59	265	72	71	.	199	59	0	.	18139	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	0
	total	215	74	574	44	131	70	553	55	218	.	284	71	992	32	1086	40	2347	26	1129	33	0	.	211	80	391	58	0	.	276	72	856	42	927	44	431	70	0	.	408	52	1153	40	1053	42	659	43	1271	34	1100	33	1177	39	379	73	964	41	275	54	0	.	263	87	0	.	17	.	0	.	698	57	151	79	1065	44	110	.	624	60	982	41	354	85	58	.	889	38	99	.	369	58	228	56	180	76	89	.	129	.	0	.	374	73	167	74	478	50	0	.	714	42	80	93	386	52	631	45	1276	31	1087	38	1144	34	862	34	741	50	131	.	150	74	631	42	231	75	54	.	0	.	0	.	502	53	146	78	0	.	80	.	654	43	616	56	198	72	346	59	57	.	0	.	0	.	1517	28	1146	42	1121	34	222	.	235	71	0	.	1093	41	698	45	265	72	71	.	454	60	0	.	43925	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	344	56	33	95	202	88	0	.	783	60	849	59	1866	30	1043	41	1863	37	1623	30	14	.	29	59	273	53	0	.	460	70	802	57	688	53	347	77	68	100	698	40	1591	34	1130	40	1215	44	2197	27	3373	22	759	43	614	63	663	40	337	77	228	100	205	74	197	71	0	.	417	71	710	63	115	100	138	91	277	56	601	59	289	64	219	74	1133	39	776	36	396	58	193	50	150	85	391	56	294	73	499	62	553	51	113	76	248	64	14	100	159	71	575	52	30	100	24	.	37	100	20	100	455	60	210	58	300	91	41	.	724	73	715	61	199	68	290	58	267	71	277	74	138	77	607	54	0	.	220	63	905	36	-6	100	284	62	59	.	169	99	375	71	232	72	0	.	1388	42	760	49	1564	35	375	61	219	66	671	52	410	49	497	48	61	71	224	76	159	100	0	.	45649	6
	broadleaves	1051	37	296	58	225	65	57	107	29	71	151	79	654	45	181	56	1313	33	1307	27	101	.	662	52	614	43	0	.	64	82	554	73	464	52	770	48	233	79	478	40	136	82	83	85	0	.	154	63	220	93	1228	34	302	55	542	61	69	82	110	100	27	70	47	76	0	.	101	100	319	62	25	93	379	73	516	65	77	84	232	58	78	100	1183	34	742	36	786	48	436	44	279	59	977	38	78	69	30	73	607	51	84	80	219	59	213	72	18	100	0	.	104	100	36	.	397	56	749	47	243	80	127	73	534	54	244	.	5	100	19	100	300	86	2	100	99	100	330	91	139	83	570	46	0	.	88	64	146	55	173	71	335	59	75	.	335	61	0	.	205	71	0	.	29	54	16	100	269	64	257	69	608	45	239	44	313	50	255	59	135	81	159	70	120	66	0	.	27160	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1395	33	328	58	427	60	57	107	812	59	1000	60	2520	28	1224	41	3176	26	2930	26	114	.	691	52	887	45	0	.	524	64	1356	45	1152	42	1117	44	301	71	1176	36	1727	32	1213	39	1215	44	2351	26	3593	21	1988	28	916	47	1205	42	405	73	338	75	231	73	245	71	0	.	517	63	1029	51	140	85	517	71	793	59	679	55	521	52	298	71	2316	32	1518	32	1182	40	629	43	429	52	1368	38	373	64	530	62	1159	40	197	71	467	51	227	71	178	72	575	52	134	80	60	.	434	58	769	46	698	50	337	56	835	48	285	.	729	73	735	60	499	64	293	58	366	72	608	62	277	71	1177	38	0	.	308	60	1051	36	167	71	619	51	134	.	504	61	375	71	437	71	0	.	1417	41	776	48	1834	32	632	50	827	45	910	48	722	45	752	46	197	76	382	60	278	71	0	.	72809	5
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	51	100	0	0	494	62	1748	31	2235	37	2224	34	6642	19	6646	18	4899	19	2384	26	0	.	275	76	369	90	252	.	432	72	1199	43	1733	34	1506	41	7	100	1681	41	3660	25	1300	35	3655	25	6269	18	4630	21	2924	28	645	50	2548	31	2346	29	398	64	647	62	390	80	464	58	1397	40	847	46	532	75	1897	38	1676	41	767	58	1431	35	1825	39	2267	33	588	56	747	48	53	.	198	71	189	59	1701	37	1627	37	1141	42	788	48	602	44	896	41	701	50	921	57	1195	37	214	64	0	.	34	100	489	58	260	74	610	62	533	62	575	53	375	71	1124	49	1298	47	1193	50	380	64	0	.	1057	41	166	100	0	.	174	.	896	48	242	61	506	53	0	.	310	51	0	.	363	73	4825	20	1839	36	4221	23	601	63	274	59	0	.	1094	43	1092	46	45	100	141	84	315	59	0	.	113886	4
	broadleaves	584	47	611	53	309	66	1918	30	195	70	607	43	1851	34	1951	31	3223	18	1339	28	165	.	202	61	612	44	0	.	292	94	631	52	398	66	642	52	153	94	189	69	406	64	478	38	25	61	194	62	78	94	2205	24	642	50	299	48	728	45	167	60	174	69	40	88	151	60	120	53	351	55	0	.	600	45	555	50	529	65	735	45	478	43	1250	39	894	39	893	40	26	.	816	45	283	60	652	49	308	70	650	54	838	41	1036	46	821	37	5	77	134	90	192	87	280	68	373	64	406	64	380	63	161	83	550	53	223	84	131	90	64	91	251	65	530	64	158	86	396	63	0	.	396	44	332	71	0	.	83	.	218	54	274	74	369	63	300	73	296	61	97	.	326	77	17	77	0	.	178	53	133	75	522	57	265	75	696	50	310	43	592	57	141	82	436	53	0	.	43507	5
	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	635	48	611	53	803	50	3666	23	2430	36	2831	33	8492	18	8598	16	8123	15	3723	22	165	.	477	61	981	50	252	.	725	58	1830	39	2131	31	2148	34	160	94	1870	39	4066	24	1778	32	3680	25	6463	18	4707	21	5129	21	1287	38	2848	31	3074	29	564	60	821	58	430	76	615	58	1517	40	1198	43	532	75	2497	34	2231	37	1296	50	2166	31	2303	37	3517	28	1482	39	1640	34	79	.	1014	43	471	59	2353	33	1934	34	1791	37	1626	39	1638	37	1718	33	706	50	1055	52	1387	35	494	50	373	64	440	60	869	43	421	61	1160	45	756	53	706	52	439	72	1375	44	1828	40	1351	47	775	52	0	.	1454	36	497	74	0	.	257	.	1114	46	516	54	875	47	300	73	606	46	97	.	689	58	4842	20	1839	36	4399	22	734	60	796	51	265	75	1790	38	1403	41	636	56	282	60	752	44	0	.	157392	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	84	71	-6	100	129	59	7	.	209	.	180	72	7423	15	3183	21	826	43	1487	28	25	72	0	.	122	63	0	.	0	.	374	59	387	51	661	52	0	.	1027	35	1722	27	1874	28	1669	30	3672	19	3877	18	1683	30	927	39	656	47	1965	28	55	100	1482	31	447	45	633	48	1590	33	154	89	1089	40	699	37	383	73	102	100	894	47	672	51	1088	37	53	100	99	73	281	72	14	.	172	46	586	45	287	56	201	49	168	70	0	.	54	59	729	46	2167	53	890	42	8	100	45	100	35	100	875	33	201	55	208	58	429	51	181	.	685	44	154	78	427	52	725	50	181	.	0	.	345	48	0	.	0	.	1166	28	197	51	527	43	407	54	13	.	21	.	0	.	257	.	3284	24	4203	18	2206	24	190	85	168	.	216	71	32	92	186	55	110	83	19	100	0	.	0	.	66855	5
	broadleaves	249	59	280	54	195	52	81	.	82	.	20	77	931	29	1719	24	356	39	810	33	293	61	0	.	188	61	0	.	84	.	24	100	539	41	146	61	0	.	694	43	320	48	334	51	235	66	524	46	103	100	1189	28	1034	44	178	87	673	41	66	77	188	39	280	41	195	69	195	42	73	83	32	66	334	55	314	83	465	60	451	42	617	48	513	48	404	58	43	70	108	98	97	.	445	42	129	69	49	65	157	70	427	51	0	.	232	59	41	81	464	54	60	97	380	42	922	34	580	47	528	40	531	41	700	44	254	91	0	.	152	74	159	73	24	94	280	60	289	.	7	.	297	54	0	.	0	.	148	49	162	71	244	49	377	58	59	.	48	.	0	.	40	.	80	56	218	47	1054	32	61	100	106	.	196	70	179	58	150	63	94	92	291	72	35	.	0	.	26308	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	334	57	274	54	325	53	88	.	291	.	200	71	8354	15	4902	18	1182	36	2297	25	318	61	0	.	310	55	0	.	84	.	398	59	926	39	807	51	0	.	1721	31	2041	25	2208	26	1903	28	4196	18	3981	18	2872	26	1961	32	834	45	2638	26	121	86	1670	30	727	42	828	47	1784	32	227	87	1120	39	1033	38	696	59	567	63	1345	38	1290	41	1601	37	457	58	143	72	389	77	111	.	617	42	715	46	336	53	358	52	595	47	0	.	286	59	770	45	2631	51	950	40	388	42	967	33	615	46	1404	27	732	38	908	37	683	59	181	.	837	44	313	61	450	52	1006	48	470	.	7	.	641	46	0	.	0	.	1314	27	359	49	771	41	785	43	71	.	69	.	0	.	297	.	3364	24	4421	18	3261	20	251	73	274	.	413	58	212	60	336	52	204	71	310	68	36	.	0	.	93163	4
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	698	33	611	36	1209	34	2124	27	3404	29	3661	26	18783	10	12234	12	10502	12	5894	15	123	50	495	50	1024	38	252	.	1144	42	3158	25	3679	21	3169	26	75	91	3906	22	8934	14	5451	17	8029	16	15033	10	14392	10	6544	16	2852	24	5653	18	5442	17	889	42	2691	24	1079	37	1185	34	3404	24	2093	31	1992	31	3685	25	2619	29	1910	32	3389	22	2760	29	4708	20	2004	24	1455	31	678	36	475	42	859	31	2779	26	3071	24	1964	29	1612	31	1251	29	1565	29	1650	30	4125	32	2324	25	485	43	86	68	150	54	2596	21	872	33	1469	35	1481	31	1814	37	2191	29	1931	32	2428	29	2601	29	1120	36	364	53	2311	25	360	55	604	38	2374	21	1349	35	1401	31	1276	30	424	51	825	39	242	69	693	54	11625	12	8310	14	9514	13	1477	35	948	33	1222	34	2568	25	2481	26	353	50	571	43	898	34	0	.	278106	2
	broadleaves	2467	21	1634	25	912	32	2472	26	347	47	1090	30	4483	18	4492	17	6479	12	4482	14	761	37	1094	34	1894	24	0	.	550	55	1532	36	2017	24	1920	27	410	57	1783	24	1044	32	1020	26	716	30	2017	22	645	43	5419	14	2271	26	1241	33	1860	25	504	35	431	33	422	32	377	43	415	35	1246	35	94	45	1708	28	1583	32	1578	34	1853	25	1499	31	3042	22	2739	20	1857	28	1040	29	1438	30	1926	24	1220	32	536	43	1506	32	1545	27	1359	36	1628	24	201	41	1363	29	483	48	1704	22	2486	21	3891	17	2482	21	1896	22	2811	20	1173	38	294	51	331	47	1053	35	822	44	780	34	1118	44	373	56	1502	25	484	52	206	38	409	34	986	30	1187	30	991	35	976	35	413	46	302	58	405	64	554	29	758	30	2162	19	559	39	1339	32	838	33	1554	27	1338	24	1380	32	733	37	1005	29	0	.	131944	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	0	.	0	.	0	.	0	0
	total	3166	20	2245	22	2121	26	4596	20	3751	28	4750	24	23266	9	16726	10	16981	10	10376	12	884	34	1589	32	2918	24	252	.	1694	35	4690	22	5695	17	5089	21	485	54	5689	18	9978	13	6471	16	8744	15	17049	9	15037	10	11963	12	5122	19	6895	17	7302	16	1393	33	3121	24	1501	32	1562	34	3819	23	3339	26	2086	29	5392	21	4202	25	3489	26	5242	19	4259	25	7750	17	4744	18	3312	23	1717	28	1913	27	2785	24	3999	22	3607	23	3470	24	3157	24	2610	26	3194	22	1851	28	5487	28	2807	23	2189	20	2572	21	4041	17	5078	15	2768	20	4280	19	2654	28	2108	34	2522	28	2985	26	3250	25	3382	26	2238	34	738	45	3813	21	845	47	809	34	2784	21	2336	27	2589	24	2267	26	1401	32	1238	33	544	60	1098	46	12179	12	9068	13	11676	12	2036	30	2287	28	2060	28	4122	22	3819	21	1733	29	1304	30	1903	26	0	.	410050	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. 2279242. Birmensdorf, Swiss Federal Research Institute WSL

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