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 NFI1-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	.	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	.	23	100	8	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1407	24
	broadleaves	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	34	.	0	.	0	.	0	.	2	.	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	.	329	37
	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	.	2	.	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	.	23	100	8	.	10	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	1736	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	862	34	337	50	122	74	70	100	3	.	0	.	0	.	0	.	0	.	31	100	158	65	19	.	0	.	20	100	438	49	0	.	439	81	770	43	389	60	66	62	20	.	307	67	35	74	0	.	0	.	44	.	50	94	0	.	0	.	30	81	8	100	31	.	0	0	51	70	0	.	0	.	22	100	0	.	35	72	0	.	0	.	0	.	99	75	67	.	0	.	0	.	66	.	4	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	72	.	0	.	10	.	0	.	0	.	22	100	188	64	61	100	0	.	0	.	22	100	0	.	0	.	10	.	0	.	34	100	82	59	0	.	50	83	38	.	4	.	155	65	0	.	0	.	0	.	0	.	0	.	5450	14
	broadleaves	150	74	33	100	72	.	21	70	0	.	101	78	421	78	77	54	209	59	3	100	42	.	9	.	0	.	0	.	0	.	43	100	48	78	82	.	24	71	26	100	14	86	19	.	15	100	0	.	0	.	32	55	5	.	14	100	163	79	0	.	0	.	8	.	1	100	0	.	0	.	0	.	6	74	22	.	19	100	4	100	14	77	0	.	57	80	0	.	21	72	1	.	0	.	14	.	46	79	0	.	0	.	0	.	51	.	0	0	51	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	-22	.	0	.	0	.	76	100	25	94	21	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	28	100	0	.	1	.	31	94	174	72	0	.	12	.	0	.	0	.	2339	20
	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	1284	34	415	44	330	61	73	100	46	.	9	.	0	.	0	.	0	.	73	72	206	64	101	.	24	71	46	100	452	49	19	.	454	78	770	43	389	60	98	56	25	.	321	66	198	68	0	.	0	.	52	.	52	95	0	.	0	.	30	81	14	77	52	.	19	100	55	70	14	77	0	.	80	85	0	.	55	72	1	.	0	.	14	.	146	63	67	.	0	.	0	.	117	.	4	77	51	.	0	.	0	.	27	.	0	.	0	.	0	.	0	.	63	72	0	.	0	.	0	.	72	.	0	.	-12	.	0	.	0	.	99	81	213	62	82	100	0	.	0	.	26	100	0	.	0	.	10	.	0	.	34	100	82	59	0	.	78	71	38	.	6	.	186	61	174	72	0	.	12	.	0	.	0	.	7789	12
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	168	56	150	60	307	62	142	72	0	.	227	101	772	34	247	46	1299	27	82	76	80	70	63	72	163	70	0	.	68	.	2	100	155	69	361	68	0	.	307	72	202	76	127	80	116	57	884	32	426	42	355	65	242	71	716	47	560	47	208	78	110	78	0	.	29	73	0	.	0	.	86	73	164	95	199	59	177	58	23	94	0	.	83	100	68	76	167	67	11	100	81	71	86	100	143	60	402	54	2	.	193	66	166	73	159	85	56	51	221	57	65	100	0	.	0	.	61	100	322	50	0	.	0	.	52	.	199	93	329	69	100	64	187	69	152	100	271	58	211	75	39	.	49	.	196	71	0	.	0	.	9	.	88	63	59	100	99	90	0	.	73	94	630	36	222	46	397	47	54	70	89	.	331	50	81	64	323	79	137	100	187	71	168	64	0	.	16237	8
	broadleaves	266	38	250	39	56	96	63	59	0	.	39	86	427	44	216	50	452	28	142	50	160	56	137	51	168	80	0	.	17	.	141	99	126	56	123	57	0	.	141	77	0	.	3	100	280	50	436	42	69	87	327	44	223	53	8	59	148	48	162	48	26	81	47	.	21	76	0	.	177	80	26	100	8	78	120	57	49	78	149	78	0	.	64	100	251	54	82	70	75	77	48	94	81	72	277	65	46	100	92	.	171	50	57	67	185	49	130	57	131	65	117	91	710	35	57	.	867	33	486	34	74	76	426	46	47	.	58	100	3	71	67	81	118	59	85	100	126	69	227	76	0	.	2	.	93	53	0	.	42	.	58	.	162	75	90	82	33	76	0	.	40	100	201	44	209	40	390	38	66	71	18	.	136	60	37	65	133	56	291	53	56	71	215	50	0	.	12632	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	.	0	.	0	0
	total	433	40	401	39	363	54	205	57	0	.	266	88	1199	29	463	41	1751	23	224	48	241	47	200	53	331	57	0	.	85	.	143	97	281	57	484	54	0	.	448	64	202	76	130	79	396	40	1320	27	495	38	682	51	465	48	724	46	708	41	370	50	135	73	47	.	50	70	0	.	177	80	112	70	172	92	320	53	226	60	172	70	0	.	146	100	318	52	249	62	86	69	129	58	168	77	420	51	448	57	95	.	364	48	223	59	344	57	187	49	352	44	183	68	710	35	57	.	928	33	809	33	74	76	426	46	99	.	258	77	331	69	167	65	305	51	237	74	397	56	438	62	39	.	51	.	289	58	0	.	42	.	67	.	250	66	149	71	132	79	0	.	113	72	830	30	432	38	787	33	120	71	107	.	467	44	119	59	457	59	428	48	242	63	383	47	0	.	28869	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	48	83	410	47	76	71	221	80	177	.	148	96	810	36	739	48	1472	30	248	44	0	.	127	100	80	91	0	.	184	81	750	44	118	97	275	98	0	.	172	68	888	46	877	46	311	57	570	52	891	37	742	46	321	72	763	41	196	65	0	.	247	91	0	.	9	.	0	.	334	69	139	83	673	63	54	.	197	72	582	46	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	.	61	100	0	.	0	.	257	61	177	77	206	62	361	58	0	.	62	86	236	67	88	88	22	.	0	.	0	.	159	79	124	75	0	.	69	.	262	68	339	87	195	72	153	80	20	.	0	.	0	.	1134	32	1110	43	776	42	208	.	160	78	0	.	796	47	382	62	0	0	0	.	255	61	0	.	23479	7
	broadleaves	168	82	163	49	55	70	332	72	41	.	136	100	112	63	347	54	833	34	881	35	0	.	84	71	166	78	0	.	92	81	106	68	422	61	156	79	0	.	230	71	108	64	45	70	55	58	702	43	117	100	435	54	59	84	200	51	79	100	0	.	16	100	0	.	8	.	0	.	299	99	11	100	311	62	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	.	472	55	0	.	187	70	595	47	1275	31	491	57	926	36	546	39	380	58	0	.	88	100	277	62	143	71	32	.	0	.	0	.	150	70	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	.	17304	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	923	34	1086	40	2305	26	1129	33	0	.	211	80	246	71	0	.	276	72	856	42	540	55	431	70	0	.	402	53	996	43	922	45	366	52	1271	34	1008	35	1177	39	379	73	964	41	275	54	0	.	263	87	0	.	17	.	0	.	634	62	151	79	984	47	110	.	624	60	865	45	354	85	58	.	889	38	99	.	369	58	228	56	180	76	89	.	129	.	0	.	374	73	167	74	478	50	0	.	673	44	74	.	249	58	595	47	1275	31	748	47	1103	35	751	36	741	50	0	.	150	74	513	46	231	75	54	.	0	.	0	.	309	59	146	78	0	.	80	.	654	43	616	56	198	72	346	59	57	.	0	.	0	.	1323	29	1143	42	914	38	222	.	235	71	0	.	1093	41	698	45	265	72	71	.	454	60	0	.	40784	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	596	68	688	53	347	77	68	100	582	45	1591	34	1049	43	1215	44	1790	31	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	1059	41	776	36	396	58	193	50	150	85	391	56	294	73	499	62	553	51	113	76	248	64	14	100	70	.	575	52	30	100	24	.	37	100	20	100	455	60	210	58	300	91	41	.	724	73	715	61	75	73	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	1466	37	219	76	219	66	671	52	410	49	497	48	61	71	224	76	159	100	0	.	44297	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	453	42	136	82	80	88	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	1170	34	742	36	786	48	436	44	279	59	977	38	78	69	30	73	607	51	84	80	219	59	213	72	0	.	0	.	104	100	36	.	397	56	749	47	243	80	81	100	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	235	75	608	45	239	44	313	50	255	59	135	81	159	70	120	66	0	.	27032	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	1149	50	1152	42	1117	44	301	71	1035	40	1727	32	1129	41	1215	44	1944	29	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	2229	33	1518	32	1182	40	629	43	429	52	1368	38	373	64	530	62	1159	40	197	71	467	51	227	71	70	.	575	52	134	80	60	.	434	58	769	46	698	50	291	63	835	48	285	.	729	73	735	60	375	79	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	1735	33	454	58	827	45	910	48	722	45	752	46	197	76	382	60	278	71	0	.	71329	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	1722	31	2004	39	2224	34	6249	20	6646	18	4899	19	2384	26	0	.	275	76	362	91	252	.	432	72	1199	43	1733	34	1506	41	7	.	1681	41	3492	26	1111	37	3655	25	5968	19	4630	21	2924	28	645	50	2548	31	2175	30	398	64	647	62	390	80	464	58	1397	40	746	51	532	75	1897	38	1676	41	464	71	1431	35	1825	39	1969	36	588	56	747	48	53	.	198	71	189	59	1701	37	1501	39	1141	42	788	48	602	44	896	41	574	58	921	57	1195	37	189	72	0	.	34	100	489	58	260	74	610	62	533	62	475	61	375	71	1124	49	1091	54	1193	50	380	64	0	.	1057	41	166	.	0	.	174	.	896	48	201	71	506	53	0	.	310	51	0	.	363	73	4472	21	1839	36	4088	23	287	73	274	59	0	.	1094	43	1092	46	45	100	141	84	315	59	0	.	110271	4
	broadleaves	584	47	611	53	309	66	1750	32	195	70	607	43	1438	33	1951	31	3223	18	1229	29	165	.	202	61	449	52	0	.	292	94	631	52	398	66	642	52	144	.	189	69	406	64	390	40	25	61	194	62	78	94	2205	24	642	50	299	48	728	45	167	60	141	83	40	88	151	60	120	53	351	55	0	.	600	45	555	50	529	65	735	45	478	43	842	49	894	39	893	40	26	.	816	45	283	60	652	49	281	76	650	54	838	41	1036	46	708	40	5	77	134	90	192	87	280	68	373	64	406	64	380	63	161	83	550	53	223	84	13	100	64	91	251	65	435	74	158	86	396	63	0	.	396	44	156	.	0	.	83	.	218	54	274	74	369	63	300	73	296	61	97	.	326	77	12	100	0	.	173	55	133	75	522	57	265	75	696	50	310	43	592	57	141	82	436	53	0	.	41575	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	3473	24	2199	39	2831	33	7686	18	8598	16	8123	15	3613	23	165	.	477	61	811	58	252	.	725	58	1830	39	2131	31	2148	34	151	.	1870	39	3898	25	1500	33	3680	25	6162	19	4707	21	5129	21	1287	38	2848	31	2903	30	564	60	788	60	430	76	615	58	1517	40	1097	46	532	75	2497	34	2231	37	993	58	2166	31	2303	37	2811	31	1482	39	1640	34	79	.	1014	43	471	59	2353	33	1782	36	1791	37	1626	39	1638	37	1604	34	579	58	1055	52	1387	35	468	52	373	64	440	60	869	43	421	61	1160	45	756	53	488	60	439	72	1375	44	1526	45	1351	47	775	52	0	.	1454	36	321	.	0	.	257	.	1114	46	475	59	875	47	300	73	606	46	97	.	689	58	4484	21	1839	36	4260	23	420	73	796	51	265	75	1790	38	1403	41	636	56	282	60	752	44	0	.	151846	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	6129	16	3183	21	826	43	1240	29	25	72	0	.	122	63	0	.	0	.	220	72	387	51	661	52	0	.	944	38	1404	30	1801	29	1068	31	2970	22	3450	20	1683	30	927	39	656	47	1828	29	55	100	1482	31	447	45	633	48	1590	33	154	89	1033	41	617	40	383	73	102	100	772	53	672	51	788	40	53	100	99	73	281	72	14	.	124	50	356	59	249	63	108	68	168	70	0	.	54	59	713	47	2124	54	753	47	8	100	45	100	35	100	517	45	182	60	55	100	429	51	181	.	669	45	154	78	381	57	573	57	181	.	0	.	345	48	0	.	0	.	1063	30	148	59	527	43	407	54	13	.	21	.	0	.	257	.	3004	26	3915	19	2061	25	190	85	168	.	216	71	3	100	186	55	110	83	19	100	0	.	0	.	60016	5
	broadleaves	249	59	280	54	195	52	81	.	82	.	20	77	698	30	1719	24	356	39	749	35	293	61	0	.	188	61	0	.	84	.	24	100	539	41	146	61	0	.	678	44	320	48	246	59	235	66	360	57	103	100	1189	28	1034	44	178	87	669	41	66	77	188	39	280	41	195	69	195	42	73	83	32	66	327	56	314	83	465	60	303	50	617	48	473	52	404	58	43	70	108	98	97	.	337	45	4	71	20	67	117	88	427	51	0	.	232	59	41	81	464	54	2	100	345	45	922	34	580	47	526	40	498	43	583	49	254	91	0	.	152	74	159	73	24	94	280	60	289	.	7	.	297	54	0	.	0	.	142	51	162	71	219	53	377	58	59	.	48	.	0	.	40	.	69	64	213	48	897	34	61	100	106	.	196	70	106	69	150	63	94	92	291	72	35	.	0	.	24721	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	6827	16	4902	18	1182	36	1989	26	318	61	0	.	310	55	0	.	84	.	244	73	926	39	807	51	0	.	1622	33	1724	27	2047	27	1302	29	3330	21	3553	19	2872	26	1961	32	834	45	2498	27	121	86	1670	30	727	42	828	47	1784	32	227	87	1065	41	944	41	696	59	567	63	1075	44	1290	41	1261	40	457	58	143	72	389	77	111	.	461	45	360	59	269	62	225	69	595	47	0	.	286	59	753	46	2589	52	754	47	353	46	967	33	615	46	1043	32	681	40	638	46	683	59	181	.	821	45	313	61	404	57	853	54	470	.	7	.	641	46	0	.	0	.	1205	28	309	54	746	43	785	43	71	.	69	.	0	.	297	.	3073	26	4128	18	2958	22	251	73	274	.	413	58	109	69	336	52	204	71	310	68	36	.	0	.	84736	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	2099	27	3173	30	3661	26	16882	10	12205	12	10502	12	5648	15	123	50	495	50	999	39	252	.	1144	42	2798	27	3240	23	3169	26	75	91	3706	23	8134	15	4965	18	6885	17	13200	11	13355	11	6544	16	2769	25	5653	18	5134	18	889	42	2691	24	1079	37	1185	34	3404	24	1945	33	1936	31	3498	26	2619	29	1540	34	3150	23	2760	29	4012	22	2004	24	1455	31	678	36	475	42	811	33	2525	28	2880	25	1871	30	1612	31	1251	29	1565	29	1417	33	4043	32	2176	26	282	53	82	71	150	54	2041	25	829	34	1171	41	1454	31	1578	41	2150	30	1690	35	2156	31	2320	32	1120	36	364	53	2207	26	360	55	604	38	2272	22	1300	36	1360	32	1276	30	393	55	825	39	242	69	693	54	10686	13	7935	14	8797	14	1007	38	947	33	1222	34	2538	25	2481	26	353	50	571	43	898	34	0	.	261157	2
	broadleaves	2467	21	1634	25	912	32	2305	27	347	47	1090	30	3750	19	4492	17	6437	12	4310	15	761	37	1094	34	1585	26	0	.	550	55	1532	36	1996	25	1920	27	401	58	1720	25	984	33	782	29	609	35	1846	23	587	47	5419	14	2264	26	1241	33	1856	25	504	35	397	35	422	32	377	43	415	35	1219	35	94	45	1631	29	1583	32	1567	34	1704	26	1499	31	2561	24	2739	20	1857	28	1040	29	1438	30	1837	24	1095	35	424	52	1466	33	1545	27	1359	36	1515	25	176	46	1252	31	415	54	1558	24	2370	22	3877	17	2127	23	1739	24	2639	21	1173	38	77	78	326	48	1053	35	727	48	711	37	1118	44	373	56	1413	26	308	59	206	38	403	34	986	30	1162	30	991	35	976	35	413	46	302	58	405	64	499	32	473	28	1867	21	537	41	1329	32	838	33	1481	28	1338	24	1380	32	729	38	1005	29	0	.	125932	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	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	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. 2279135. Birmensdorf, Swiss Federal Research Institute WSL

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