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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NFI4
growing stock (stemwood)
development stage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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	.	3	559	0	.	0	.	0	.	0	.	416	36	94	70	0	.	0	0	0	.	0	.	0	.	0	.	7	100	0	.	126	71	0	.	0	.	0	.	25	.	0	.	22	100	579	39	54	63	0	.	11	.	8	.	3	100	0	.	0	.	0	.	0	.	0	.	9	.	11	.	4	89	5	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	71	45	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	27	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	55	3	100	97	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1825	18
	broadleaves	0	.	14	75	0	.	0	.	0	.	0	.	0	0	2	100	14	100	13	72	0	.	0	.	0	.	0	.	8	58	0	.	15	82	0	.	5	.	0	.	0	.	13	.	21	100	0	.	0	.	0	.	6	.	0	.	28	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	8	100	0	.	3	100	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	13	94	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-11	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	200	26
	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	.	17	101	0	.	0	.	0	.	0	.	416	36	96	68	14	100	13	72	0	.	0	.	0	.	0	.	15	72	0	.	141	70	0	.	5	.	0	.	25	.	13	.	43	71	579	39	54	63	0	.	17	.	8	.	31	73	0	.	0	.	0	.	0	.	0	.	9	.	11	.	4	76	13	100	0	.	3	100	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	137	65	46	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-11	.	0	.	0	.	7	.	0	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	55	3	100	97	71	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	2025	17
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	40	95	54	64	1	100	155	53	0	.	117	67	580	33	487	39	118	45	6	75	26	100	0	.	9	100	0	.	0	.	0	0	111	46	98	63	0	.	93	90	318	42	178	70	137	97	558	31	287	47	75	60	11	81	116	85	115	64	86	.	0	.	0	.	7	100	0	.	1	100	22	53	42	91	38	68	62	100	145	68	12	71	39	94	11	100	22	.	2	100	44	68	0	.	1	100	78	68	17	92	20	68	0	0	13	100	7	71	65	64	10	96	47	78	0	.	0	.	0	.	0	.	3	100	82	82	0	.	16	54	96	60	77	66	31	100	95	66	0	.	2	75	11	89	6	100	0	0	0	0	16	100	10	71	31	.	18	100	0	.	0	0	198	46	104	54	280	71	33	70	40	.	104	71	92	77	5	77	0	.	37	100	106	84	0	.	5974	10
	broadleaves	238	46	111	46	49	95	132	59	0	.	43	72	86	64	55	48	255	41	65	63	3	79	4	100	25	98	0	.	0	.	29	59	58	53	26	78	0	.	57	57	6	89	27	91	0	.	27	74	2	100	29	50	120	76	19	61	26	62	0	.	0	.	0	.	23	100	0	.	24	88	15	71	34	65	35	74	21	100	26	73	40	60	98	61	21	100	7	.	0	0	82	75	0	.	67	92	5	100	1	100	24	66	0	.	37	75	2	83	0	.	0	100	17	76	16	100	2	.	2	.	2	100	36	72	22	67	0	.	12	71	18	74	1	100	0	.	22	69	54	100	-7	100	5	84	3	100	0	.	26	91	0	.	27	95	0	.	10	75	0	.	8	80	3	64	47	62	18	86	23	100	4	.	36	82	72	66	1	70	42	50	13	71	83	67	0	.	2673	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	278	47	164	40	50	93	287	44	0	.	160	63	667	30	542	37	373	33	71	59	30	97	4	100	34	98	0	.	0	.	29	59	170	39	124	57	0	.	150	59	324	41	206	62	137	97	585	31	289	47	104	50	131	70	136	74	141	60	86	.	0	.	0	.	30	100	0	.	25	86	37	57	76	78	72	70	83	100	171	59	52	56	137	53	32	100	29	.	2	100	127	57	0	.	68	91	83	65	18	90	44	51	0	0	50	70	9	57	65	64	10	92	63	61	16	100	2	.	2	.	2	100	39	67	104	66	0	.	28	54	114	59	77	65	31	100	117	64	54	100	-5	128	16	87	9	77	0	0	26	91	16	100	37	86	31	.	27	90	0	.	8	80	202	45	151	54	298	67	56	60	44	.	139	70	164	57	6	65	42	50	50	87	189	57	0	.	8647	8
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	89	48	419	36	230	49	141	63	42	100	127	71	813	26	290	53	955	22	206	54	0	.	92	52	96	72	0	.	137	62	226	53	140	62	144	52	0	.	209	43	270	39	286	45	722	26	715	24	1093	22	671	32	439	37	494	37	307	37	24	100	126	62	14	72	5	78	117	.	187	62	174	65	205	68	139	51	190	54	86	49	17	93	122	64	285	48	101	47	14	161	42	76	42	71	169	42	315	43	29	72	222	65	215	48	337	54	49	54	198	39	41	58	45	63	0	.	31	100	343	30	120	57	77	77	11	155	97	96	91	67	210	44	155	63	85	54	90	68	89	73	169	54	48	59	65	75	0	.	134	69	154	80	41	98	121	77	86	80	0	.	51	68	917	24	498	27	780	28	168	58	256	61	93	66	322	49	118	92	162	74	67	63	143	52	0	.	18655	5
	broadleaves	242	56	189	40	216	36	249	67	55	95	18	77	112	35	181	34	515	22	125	54	77	75	28	64	197	53	0	.	133	53	44	74	109	52	189	51	10	100	211	41	97	54	6	100	377	30	596	29	126	48	355	31	110	52	88	56	61	39	85	48	39	55	6	84	16	80	0	.	69	87	35	72	23	96	155	48	68	70	68	51	84	71	15	127	140	53	118	59	24	83	188	48	37	107	260	43	169	48	76	66	209	52	172	41	214	40	121	51	210	35	222	41	630	31	286	32	1037	22	813	23	239	37	509	31	29	100	95	59	27	97	61	98	78	56	110	43	51	74	45	75	133	42	26	84	33	61	0	.	75	97	50	69	74	58	56	72	2	100	0	.	18	68	201	28	258	29	719	23	45	54	19	76	80	51	142	59	72	49	128	53	28	92	292	40	0	.	13994	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	.
	total	330	45	608	33	445	35	391	50	97	71	145	68	925	24	470	39	1470	18	331	43	77	75	119	49	292	49	0	.	270	49	270	51	249	48	332	40	10	100	420	34	366	35	292	45	1099	20	1310	20	1219	20	1026	28	549	35	582	33	368	34	109	45	165	56	20	74	21	73	117	.	257	51	209	63	228	62	294	43	258	45	154	40	101	62	138	60	425	38	219	44	37	79	229	46	79	80	429	34	484	37	105	60	431	43	387	39	551	41	170	47	408	28	263	36	675	29	286	32	1068	22	1156	19	360	32	585	29	40	114	192	57	118	56	271	44	232	47	195	36	142	53	134	73	302	44	74	59	99	63	0	.	209	58	204	66	115	61	176	60	88	80	0	.	69	65	1118	20	756	22	1499	20	214	51	275	57	172	48	463	41	190	61	289	51	95	59	435	39	0	.	32649	4
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	157	55	105	74	430	54	243	47	114	78	258	74	1017	26	396	40	922	29	889	31	118	81	59	84	49	75	0	.	89	77	672	46	247	57	489	48	75	69	431	53	734	35	505	32	813	32	797	34	1227	23	461	42	362	43	524	42	330	58	120	.	221	65	34	.	0	.	58	71	332	51	176	65	422	48	330	50	380	59	492	43	110	72	388	59	429	40	213	57	144	58	9	100	230	55	356	57	211	55	83	86	186	62	108	100	433	47	50	72	287	47	3	100	48	62	0	.	78	100	305	41	155	53	206	68	255	50	121	59	29	71	25	83	153	59	96	81	136	92	105	84	282	52	0	.	127	59	304	42	167	72	116	71	187	84	9	100	205	69	84	.	56	.	892	30	987	30	993	28	360	59	41	73	243	51	190	55	196	66	108	73	0	.	433	43	0	.	26009	5
	broadleaves	354	37	162	51	47	65	362	49	21	71	109	96	386	32	340	43	822	27	934	24	64	93	147	55	187	57	0	.	69	62	206	75	366	48	315	61	200	51	252	52	54	78	131	66	132	49	384	34	207	59	663	31	79	55	119	51	13	100	0	.	105	93	23	.	0	.	42	79	176	75	3	100	346	48	227	57	151	53	142	54	222	62	267	44	677	28	95	54	287	41	74	81	512	36	134	60	41	86	149	64	190	77	30	100	63	75	75	58	182	47	92	82	521	33	618	36	722	28	345	47	746	29	608	33	293	49	6	100	68	100	44	80	125	58	83	87	121	73	40	83	230	48	0	.	242	55	87	77	236	42	305	54	241	53	89	71	11	82	43	.	1	.	186	50	167	47	265	39	53	60	108	77	166	45	285	43	157	65	186	67	165	74	82	54	0	.	19078	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	.
	total	512	34	267	49	477	51	606	39	134	76	366	60	1403	23	736	33	1744	22	1823	23	182	71	207	51	236	52	0	.	157	68	878	40	614	39	804	42	275	50	683	43	788	34	636	32	945	29	1181	26	1434	22	1124	28	442	41	643	41	343	60	120	.	325	59	57	.	0	.	100	74	508	44	179	65	767	43	557	45	531	46	635	39	333	58	655	50	1106	25	308	50	431	37	83	76	742	35	490	50	252	51	232	64	376	54	138	81	496	43	126	51	469	35	94	82	569	31	618	36	799	27	650	34	901	28	814	30	548	44	128	59	97	85	69	71	278	56	179	63	257	66	145	73	512	40	0	.	369	46	391	45	402	41	421	47	428	55	99	71	216	66	127	.	57	.	1078	27	1154	26	1258	26	413	56	149	73	409	43	475	41	352	49	293	55	165	74	515	42	0	.	45086	4
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	246	40	94	66	382	40	268	57	1057	34	1041	40	2822	20	1285	25	1331	29	1431	24	92	92	82	67	272	39	0	.	357	58	972	35	1654	29	575	45	0	.	1071	36	1234	28	1688	23	1557	27	2664	21	4383	14	2433	20	227	76	310	43	795	36	167	62	198	66	290	52	24	.	321	47	111	55	190	80	475	38	402	50	441	48	877	34	348	49	1270	34	931	27	107	60	661	34	240	48	355	34	380	46	873	43	826	40	648	37	474	50	296	46	0	.	570	41	681	36	78	67	0	.	0	.	387	40	166	51	348	50	260	73	541	49	647	47	171	54	130	56	1058	37	67	74	50	.	318	49	118	74	254	54	799	34	35	97	143	63	108	73	223	86	71	.	92	100	98	71	1130	28	1030	31	1844	23	291	55	332	64	428	42	309	43	779	34	222	52	540	44	81	.	0	.	55628	4
	broadleaves	905	28	468	40	315	40	431	42	217	43	361	48	1396	31	645	31	1085	25	1209	25	88	88	655	35	862	32	0	.	29	76	379	44	234	40	856	36	72	.	601	35	162	54	200	48	11	91	218	57	6	71	2080	18	579	61	310	58	40	63	142	57	44	62	129	72	54	.	83	54	400	44	178	73	303	61	122	59	474	44	332	36	194	54	672	34	1033	25	425	45	702	29	347	43	921	27	124	50	17	101	386	49	263	42	375	42	521	40	0	.	15	73	55	82	197	48	276	53	449	42	112	69	173	53	379	46	8	100	61	96	12	84	313	57	100	62	307	42	215	62	0	.	271	53	187	63	124	45	75	53	77	79	423	44	103	72	446	40	0	.	76	71	27	100	8	64	38	77	199	47	196	51	331	41	130	46	221	45	415	33	440	50	309	52	29	.	0	.	29452	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
	total	1151	26	562	36	697	34	699	37	1273	32	1402	35	4217	19	1930	24	2416	21	2640	21	180	71	738	34	1135	30	0	.	385	55	1351	30	1887	27	1432	31	72	.	1671	30	1396	26	1888	23	1568	27	2882	20	4389	14	4513	15	806	49	620	44	835	36	310	46	242	63	419	47	78	.	404	45	511	40	368	58	778	38	524	47	915	36	1209	29	543	42	1942	28	1964	22	532	43	1363	25	587	36	1276	26	504	43	891	43	1212	34	912	36	849	40	817	36	0	.	584	41	736	34	275	43	276	53	449	42	499	42	340	46	726	38	268	72	603	48	659	47	484	47	230	45	1365	34	281	55	50	.	590	40	305	61	378	47	874	34	112	71	567	42	211	57	669	43	71	.	168	82	126	71	1138	28	1068	30	2043	23	487	46	663	43	558	39	530	42	1193	29	662	42	849	38	110	.	0	.	85080	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	526	38	220	60	453	50	1747	26	1101	37	2868	25	6401	16	7480	15	5856	14	2499	24	176	78	453	53	323	57	0	.	438	51	753	43	1279	32	1304	35	5	100	1644	29	3676	20	1797	28	3275	19	4712	16	3972	22	1956	23	1442	33	2607	23	2381	28	330	53	784	39	557	52	418	46	1860	32	373	50	573	51	1276	32	1005	38	524	48	1407	34	1326	29	2869	25	503	42	983	32	134	71	176	80	377	48	1796	27	1202	30	1035	34	541	68	294	50	476	43	532	39	1034	32	933	35	52	100	108	100	14	100	364	45	120	69	282	65	127	100	435	53	415	58	1069	39	1010	36	1443	32	448	46	131	92	760	43	296	56	276	55	381	42	1114	30	514	37	841	34	0	.	508	49	327	69	550	43	4995	15	1450	31	2531	22	398	50	809	38	396	52	957	34	919	35	408	60	277	53	506	50	0	.	109897	3
	broadleaves	1355	27	664	38	547	38	1240	31	106	70	833	38	1708	27	1035	28	2959	15	1082	22	480	45	585	39	1141	32	0	.	586	54	561	44	609	44	766	40	172	82	355	40	274	62	190	44	28	57	137	59	55	100	1456	23	872	32	409	33	572	38	366	42	152	55	250	54	169	76	195	39	171	51	0	.	561	46	691	35	477	48	499	41	841	33	689	31	753	34	1065	27	131	60	638	37	605	43	461	41	200	68	515	45	255	60	524	53	845	39	9	81	196	51	15	67	115	77	477	55	233	47	346	50	151	72	200	60	144	90	8	100	28	93	134	69	251	69	221	49	511	43	42	78	217	52	730	41	-4	100	75	48	316	34	422	43	359	51	111	.	282	51	97	66	644	40	30	59	11	77	130	49	296	51	702	38	741	41	530	39	604	33	218	60	331	47	500	41	0	.	42253	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	1882	25	884	37	1000	32	2987	21	1206	37	3700	24	8108	14	8515	14	8815	12	3581	20	656	42	1039	37	1464	31	0	.	1024	39	1314	35	1888	28	2071	30	177	82	1999	27	3950	19	1987	27	3303	19	4849	16	4026	22	3411	19	2315	26	3016	23	2953	25	696	38	935	37	807	44	587	42	2055	31	545	47	573	51	1837	29	1696	30	1001	36	1906	31	2167	26	3558	23	1256	33	2048	24	265	60	815	36	982	37	2257	25	1402	29	1550	31	796	52	818	45	1321	33	541	39	1231	30	949	35	168	61	585	48	248	46	709	34	271	57	482	46	271	71	443	52	444	58	1203	37	1261	33	1664	31	959	41	173	88	976	38	1027	36	272	56	456	42	1431	29	936	31	1200	31	111	.	790	42	424	63	1194	33	5025	15	1461	31	2660	21	694	41	1512	31	1137	34	1487	31	1523	29	626	49	608	43	1006	38	0	.	152150	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	62	71	251	82	241	59	236	58	79	60	179	45	6841	12	4949	14	901	36	1580	24	6	59	0	.	89	48	101	.	2	100	315	46	893	34	373	43	0	.	905	28	1871	24	1795	22	1734	22	5386	13	3094	15	1612	19	600	34	308	34	1761	22	166	63	987	25	234	45	610	36	1495	24	285	57	1114	28	854	29	517	46	90	71	1017	27	412	55	899	32	0	.	126	54	293	46	56	72	77	52	497	37	127	61	148	46	227	48	29	71	49	64	754	29	971	30	853	30	25	76	52	54	28	74	777	26	255	48	169	46	503	34	109	72	818	32	300	49	475	44	949	36	108	68	3	100	650	38	129	90	117	74	1161	23	245	69	451	33	541	35	24	95	78	81	72	90	33	.	3972	15	3955	15	2599	17	362	43	27	.	1	.	255	55	181	38	65	74	38	74	39	100	0	.	68616	3
	broadleaves	327	52	198	58	92	59	212	49	42	90	88	61	932	23	1409	18	607	30	867	25	256	52	0	.	191	56	0	.	97	71	107	90	443	38	189	58	0	.	458	35	244	41	254	43	139	59	485	31	99	66	2012	19	357	45	254	57	747	31	71	68	138	37	111	51	205	38	216	36	83	67	39	40	345	41	259	54	18	100	321	36	450	42	502	43	93	.	108	57	249	44	135	72	374	47	291	44	9	69	84	69	506	41	153	58	81	61	83	65	453	36	112	76	584	46	951	27	843	28	1124	25	668	34	744	33	182	56	0	.	70	73	444	42	176	53	216	47	171	46	22	79	544	44	180	63	10	100	119	40	55	98	406	46	323	44	49	100	251	61	5	100	76	.	158	36	70	61	878	26	91	61	60	.	71	.	222	47	212	43	98	71	60	100	69	60	0	.	26799	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	389	47	449	54	333	52	449	48	121	60	267	46	7773	12	6357	13	1508	26	2447	21	262	52	0	.	279	50	101	.	99	71	422	46	1336	30	561	38	0	.	1364	26	2115	22	2049	21	1874	21	5871	12	3194	14	3623	16	957	31	563	38	2509	21	237	61	1125	25	346	46	814	34	1711	23	369	57	1152	27	1199	27	776	39	109	72	1338	24	862	38	1401	33	93	.	234	47	542	43	191	72	450	45	787	36	136	60	232	50	732	37	182	58	130	58	837	29	1424	24	965	28	609	45	1003	26	871	28	1900	18	923	29	914	29	685	36	109	72	887	31	744	35	650	38	1165	33	278	51	25	81	1194	38	310	64	127	75	1280	22	300	60	856	33	864	30	73	75	328	57	77	86	109	.	4131	15	4025	15	3477	15	453	42	87	.	72	.	477	47	393	36	163	71	99	87	108	70	0	.	95415	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	1121	22	1146	27	1737	23	2791	19	2391	23	4590	19	18890	8	14980	9	10083	10	6610	12	419	45	687	37	838	27	101	.	1030	31	2938	20	4450	16	2983	20	81	65	4354	16	8128	12	6249	13	8259	11	15411	8	14111	9	7208	11	3093	19	4368	16	5693	15	893	30	2316	19	1129	30	1064	27	3850	18	1299	25	2259	21	3277	17	2437	21	1687	25	4024	16	2226	22	5586	16	2160	18	1553	23	1249	24	567	34	1080	24	3199	18	2931	19	2183	23	1845	26	1121	28	1604	23	1396	22	3124	16	2521	19	295	32	161	69	151	58	2175	15	817	24	1085	28	1239	26	1304	29	2016	23	1871	25	1999	22	3662	19	944	29	377	45	2180	22	609	37	846	28	2672	16	1695	24	1393	21	1728	22	408	53	964	32	575	46	789	33	12246	8	8026	11	9123	10	1613	23	1505	27	1263	25	2125	20	2196	21	964	32	959	30	1309	26	0	.	286604	1
	broadleaves	3421	15	1806	19	1265	21	2627	19	440	31	1451	26	4620	15	3667	12	6257	10	4295	11	968	29	1420	23	2603	19	0	.	921	36	1326	26	1834	20	2341	21	458	41	1934	17	836	27	821	23	710	22	1847	16	494	32	6594	10	2123	23	1200	23	1486	22	664	27	477	29	519	34	467	34	537	23	924	27	270	49	1613	24	1497	22	1210	27	1392	20	1831	21	2245	18	2717	15	1818	20	1392	20	1464	22	2448	18	1337	21	454	36	1212	27	1447	23	1254	27	1760	23	291	32	1057	21	495	30	2064	19	2639	17	3286	13	2740	15	1979	18	2475	16	677	33	170	48	217	43	1013	28	730	30	937	23	1080	27	203	40	1388	23	1128	30	408	36	366	27	785	23	1606	22	1127	25	750	30	556	38	221	43	774	35	587	21	592	21	2210	14	704	28	1225	26	1223	27	1492	20	1461	19	1112	27	907	29	1055	24	0	.	134450	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
	total	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	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. 2300226. Birmensdorf, Swiss Federal Research Institute WSL

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