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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NFI1
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 1983/85
		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	77	75	38	100	12	.	331	71	14	.	0	.	452	39	474	52	58	100	61	60	0	.	0	.	0	.	0	.	0	.	120	63	702	53	0	.	0	.	97	100	0	.	110	72	72	.	63	100	267	57	0	.	5	100	151	71	50	.	0	.	0	.	0	.	31	100	210	75	0	.	148	98	27	100	0	.	184	.	34	.	0	.	12	.	211	76	0	.	32	.	0	.	0	.	0	.	0	.	0	.	377	74	0	.	18	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	19	100	0	.	33	100	0	.	0	.	0	.	52	.	92	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	64	.	0	.	60	.	116	.	150	75	66	.	232	78	0	.	322	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5677	15
	broadleaves	99	71	25	71	35	.	0	.	0	.	0	.	114	55	58	53	8	100	62	77	188	71	200	72	0	.	0	.	0	.	2	100	76	73	0	.	0	.	168	100	0	.	15	67	0	.	3	100	5	71	15	100	94	100	106	71	8	.	263	.	0	.	0	.	0	0	25	71	0	.	7	100	6	100	0	.	88	.	0	.	2	100	117	.	51	76	0	.	36	.	0	.	0	.	11	100	0	.	0	.	8	100	0	.	34	.	0	.	0	.	0	.	0	.	0	.	26	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	0	.	3	.	16	.	15	.	7	.	33	100	3	.	63	60	0	.	0	.	0	.	18	.	0	.	0	.	127	.	0	.	0	.	2273	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	176	72	63	80	48	.	331	71	14	.	0	.	566	36	532	52	66	89	123	56	188	71	200	72	0	.	0	.	0	.	122	63	778	51	0	.	0	.	265	73	0	.	125	65	72	.	67	95	272	56	15	100	99	96	256	71	57	.	263	.	0	.	0	.	31	100	235	74	0	.	155	93	34	100	0	.	272	.	34	.	2	100	129	.	262	76	0	.	69	.	0	.	0	.	11	100	0	.	0	.	386	73	0	.	52	.	0	.	0	.	9	.	0	.	0	.	26	.	7	.	19	100	0	.	33	100	0	.	0	.	0	.	52	.	92	.	0	.	0	.	0	.	0	.	0	.	23	.	26	.	0	.	0	.	67	.	16	.	75	.	124	.	183	71	68	.	295	63	0	.	322	79	0	.	18	.	0	.	0	.	127	.	0	.	0	.	7950	13
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	86	90	12	91	5	100	0	.	0	.	7	100	824	36	98	78	168	60	3	100	0	.	0	.	0	.	0	.	1	.	52	75	8	158	13	100	0	.	1	100	220	54	0	.	161	70	294	69	124	72	0	.	49	93	35	73	90	74	58	.	41	100	0	.	0	.	0	.	3	.	0	.	0	.	12	100	0	.	129	95	84	.	26	.	20	100	60	100	0	.	0	.	0	.	64	100	131	73	0	.	17	100	0	.	83	71	10	100	51	80	0	.	17	100	0	.	0	.	1	100	0	.	12	100	0	.	0	.	82	95	26	100	46	94	0	.	0	.	5	100	5	84	0	.	0	.	0	.	2	100	0	.	0	.	0	.	82	.	0	.	0	.	160	56	66	59	108	71	75	100	0	.	39	.	13	100	0	.	65	100	0	.	72	71	0	.	3917	14
	broadleaves	90	100	56	97	14	100	0	.	0	.	0	.	32	65	17	100	93	52	62	100	0	.	74	100	0	.	0	.	0	.	28	100	4	100	0	.	0	.	5	100	31	96	0	.	19	78	1	357	13	92	0	.	0	.	8	71	23	50	0	.	1	633	0	.	0	.	0	.	0	.	0	.	4	75	0	.	30	.	0	.	37	.	2	.	64	100	2	100	0	.	15	91	0	.	22	100	20	100	0	.	0	.	3	.	0	.	0	.	12	100	0	.	21	57	12	.	11	.	3	71	0	.	2	100	7	.	0	.	0	.	0	.	6	87	0	.	0	.	0	.	28	93	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	60	11	85	9	74	0	.	0	.	0	.	-2	100	1	100	4	100	0	.	42	90	0	.	949	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	176	68	68	96	20	100	0	.	0	.	7	100	856	35	115	73	261	51	66	100	0	.	74	100	0	.	0	.	1	.	79	71	13	137	13	100	0	.	7	83	251	52	0	.	180	63	295	69	137	66	0	.	49	93	43	72	113	62	58	.	42	99	0	.	0	.	0	.	3	.	0	.	4	75	12	100	30	.	129	95	121	.	29	.	84	80	62	96	0	.	15	91	0	.	86	79	151	75	0	.	17	100	3	.	83	71	10	100	62	67	0	.	38	66	12	.	11	.	4	73	0	.	15	85	7	.	0	.	82	95	26	100	52	84	0	.	0	.	5	100	33	82	0	.	0	.	0	.	6	74	0	.	0	.	0	.	82	.	0	.	0	.	168	53	77	54	116	66	75	100	0	.	39	.	11	100	1	100	70	100	0	.	114	75	0	.	4866	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	212	50	69	71	197	50	95	100	75	.	262	74	600	35	423	49	1057	31	359	42	0	.	94	66	136	62	93	.	50	84	179	58	302	64	165	71	40	71	284	78	286	51	236	59	-1	100	657	36	327	39	549	45	285	83	313	68	333	59	24	100	101	.	91	100	0	.	43	.	152	78	309	63	167	72	290	56	53	72	157	51	3	100	95	51	380	47	18	81	0	.	109	56	18	100	141	50	312	77	121	73	74	84	106	58	52	.	42	71	0	.	0	.	2	100	0	.	3	100	81	59	57	89	6	100	121	64	69	100	444	63	28	100	0	.	0	.	332	64	0	.	74	100	262	77	45	.	177	53	7	.	316	43	40	71	33	67	206	64	0	.	0	.	1149	28	663	32	304	65	42	78	31	87	255	58	182	46	466	42	0	.	0	.	122	68	0	.	16050	7
	broadleaves	437	44	132	72	192	59	276	60	20	.	12	77	212	50	118	73	604	33	565	38	0	.	279	51	195	55	36	.	47	75	210	72	204	57	121	100	213	54	69	77	19	100	29	100	299	59	612	32	44	70	302	40	41	79	4	100	3	100	27	100	0	.	43	72	0	.	21	.	146	61	20	90	263	61	152	56	73	100	134	67	267	66	148	54	477	39	41	100	117	71	117	71	233	71	138	60	17	71	94	76	69	73	14	66	9	.	47	69	46	55	45	95	374	39	461	38	1201	24	371	41	534	37	573	40	204	52	56	82	2	74	78	100	31	.	0	.	256	78	37	.	59	84	0	.	39	.	5	100	65	.	492	40	236	71	296	50	70	64	0	.	0	.	194	44	33	77	272	52	85	100	170	88	49	56	362	51	179	61	95	.	185	65	12	86	0	.	15132	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	649	38	201	54	390	51	371	52	95	.	274	74	813	31	541	44	1662	25	924	35	0	.	373	47	331	54	128	.	97	72	389	49	507	45	286	75	253	51	353	69	305	50	265	57	297	59	1269	25	371	36	851	35	326	75	317	67	337	59	51	71	101	.	134	83	0	.	64	.	299	50	329	61	430	52	442	55	126	85	291	55	270	66	243	48	858	36	59	94	117	71	226	58	252	70	279	42	330	76	215	58	144	56	120	58	62	.	89	64	46	55	45	95	376	39	461	38	1205	24	452	36	591	35	578	39	325	53	126	73	447	63	106	78	31	.	0	.	587	69	37	.	133	73	262	77	84	.	182	54	72	.	808	35	276	65	329	49	276	61	0	.	0	.	1342	25	696	32	576	44	127	72	200	88	304	52	544	45	644	40	95	.	185	65	134	64	0	.	31181	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	443	62	256	81	70	83	1195	36	361	72	1235	41	2319	27	1313	32	1075	37	1421	32	9	.	0	.	213	59	0	.	20	.	736	61	910	47	1475	36	0	.	840	48	210	87	321	74	120	.	712	59	2163	29	1398	38	117	71	601	41	1600	32	77	91	51	.	121	.	103	87	466	52	219	93	0	.	330	54	330	56	0	.	521	52	0	.	440	55	992	37	8	100	374	69	215	100	511	35	131	75	599	60	335	76	199	.	126	100	68	71	0	.	272	58	242	100	0	.	24	100	32	73	46	107	91	100	182	91	297	71	316	.	599	56	121	52	313	93	378	54	262	61	344	91	125	48	203	.	178	75	429	51	157	88	362	58	536	39	45	100	126	.	69	.	4	.	390	53	783	45	1272	39	0	.	96	72	192	93	533	56	525	48	433	58	349	71	0	.	0	.	37676	6
	broadleaves	505	43	314	59	181	71	411	43	109	100	420	39	508	40	335	44	780	33	685	33	77	.	158	.	374	49	0	.	209	.	152	79	274	50	582	47	0	.	472	51	248	59	13	100	0	.	87	100	0	.	1455	30	262	67	172	64	319	62	73	100	51	.	42	.	18	90	144	61	184	64	0	.	312	66	355	49	373	71	114	87	0	.	488	64	601	35	405	88	202	64	129	92	886	32	0	.	14	100	262	73	0	.	134	91	70	80	0	.	50	91	102	100	189	61	539	39	415	48	124	71	99	61	335	50	0	.	0	.	80	100	352	55	174	85	210	52	75	80	41	100	309	51	16	.	121	53	22	56	113	61	445	50	290	63	321	63	53	.	34	.	29	.	0	.	22	61	207	57	0	.	298	75	187	88	310	56	185	45	83	63	77	98	0	.	0	.	19870	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	948	42	570	53	251	71	1607	32	470	76	1655	39	2827	25	1648	30	1855	29	2106	27	86	.	158	.	587	50	0	.	229	.	888	53	1184	42	2057	33	0	.	1312	39	458	56	334	75	120	.	799	57	2163	29	2853	25	380	59	774	39	1919	31	151	71	102	.	162	.	120	88	610	48	403	64	0	.	642	47	684	45	373	71	635	47	0	.	928	50	1592	33	412	87	576	57	344	74	1397	30	131	75	613	59	597	58	199	.	261	71	138	73	0	.	322	52	344	76	189	61	564	40	448	47	171	58	191	76	517	47	297	71	316	.	679	52	472	47	487	69	588	49	337	64	385	83	434	45	219	.	299	61	451	50	269	68	807	45	826	37	366	61	179	.	103	.	34	.	390	53	805	44	1479	35	0	.	393	64	379	71	844	49	710	42	516	59	426	72	0	.	0	.	57546	5
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	510	67	750	46	421	64	2148	31	1552	37	1364	40	6902	18	5796	19	6309	18	2266	27	122	96	30	86	517	72	0	.	747	55	495	72	1175	49	1656	40	6	.	1001	45	1867	33	1211	30	2953	26	3301	25	2070	29	2174	25	1419	38	3645	26	2113	31	418	72	157	.	644	54	100	.	283	71	485	56	572	60	1394	51	883	51	424	64	1009	42	529	69	1092	41	264	74	775	42	301	51	711	54	303	52	567	46	1797	37	370	66	646	52	550	48	625	42	377	67	349	50	806	35	66	.	0	.	0	.	231	74	149	100	108	100	350	81	441	61	469	59	468	70	326	70	478	63	554	65	0	.	317	52	107	75	793	46	376	58	95	.	97	.	798	47	170	.	0	.	105	.	906	62	919	39	734	44	1802	27	501	49	618	44	1226	37	1063	45	901	38	366	64	338	66	919	41	0	.	87743	4
	broadleaves	1009	39	442	41	151	76	908	38	98	56	218	75	1104	34	1672	28	2741	23	894	31	302	59	178	87	344	47	0	.	34	75	1	100	242	73	68	71	177	.	402	49	102	75	161	45	31	100	24	91	52	92	2096	23	525	43	340	49	760	38	113	78	15	.	94	61	19	.	26	100	364	65	69	100	788	57	318	85	521	65	611	42	103	86	387	43	421	48	406	46	458	43	563	45	390	51	320	58	37	74	146	77	271	60	279	50	455	43	0	.	32	72	14	84	11	.	203	83	0	.	0	.	185	64	251	67	236	63	0	.	51	75	42	74	37	73	10	100	427	57	0	.	245	73	210	71	199	49	43	61	72	.	42	.	142	63	54	.	148	.	32	.	132	80	1	100	0	.	32	61	230	55	430	51	253	49	296	62	726	48	363	59	283	65	290	42	0	.	27974	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1519	38	1192	40	572	53	3056	27	1650	37	1583	41	8006	17	7468	18	9050	16	3161	24	424	63	207	87	861	48	0	.	781	55	496	72	1416	44	1724	40	183	.	1402	40	1970	32	1373	30	2984	26	3325	25	2122	28	4270	20	1944	32	3984	25	2873	28	531	63	172	.	738	53	120	.	310	71	849	51	641	58	2182	41	1201	46	945	47	1620	35	632	63	1479	39	686	47	1182	38	759	42	1274	40	693	47	887	39	1834	37	516	53	917	48	829	44	1080	39	377	67	381	48	820	35	78	.	203	83	0	.	231	74	334	63	359	65	586	62	441	61	520	60	511	67	364	67	488	64	980	48	0	.	562	47	317	72	992	45	420	57	167	.	139	.	940	44	223	.	148	.	137	.	1037	56	920	39	734	44	1834	27	731	45	1048	40	1480	35	1359	41	1628	38	729	50	620	58	1209	39	0	.	115717	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	72	78	889	59	63	100	557	69	453	76	907	59	3493	27	2741	29	3863	22	1076	35	225	100	176	.	58	76	0	.	0	.	446	58	674	75	204	61	0	.	1483	38	2108	33	1191	39	2440	29	2741	25	2090	27	1390	29	1046	48	266	64	842	58	0	.	81	.	148	.	498	61	308	77	112	.	364	.	314	74	742	50	914	62	797	52	667	51	1913	39	308	63	945	50	126	71	287	.	0	.	535	58	162	100	1145	40	0	.	763	49	628	44	380	54	935	44	278	80	0	.	0	.	197	.	90	100	0	.	307	75	145	83	218	.	0	.	171	71	232	.	181	70	303	.	0	.	1130	40	121	73	0	.	0	.	0	.	0	.	368	71	0	.	338	89	172	.	0	.	1760	28	0	.	1315	36	130	.	0	.	322	.	586	53	465	61	502	60	616	61	154	.	0	.	54662	6
	broadleaves	734	51	161	80	199	99	178	95	14	100	157	95	759	49	980	39	2060	26	454	43	486	67	25	.	278	78	0	.	0	.	263	62	35	100	455	60	0	.	204	59	512	68	267	51	14	93	113	100	57	86	1055	34	564	55	283	76	6	76	0	.	16	.	10	.	92	96	38	100	0	.	0	.	124	87	264	80	97	72	80	95	274	74	103	75	303	51	550	49	175	78	1	.	0	.	63	71	250	99	442	53	0	.	262	65	653	45	15	93	185	100	0	.	44	.	0	.	25	.	207	97	0	.	264	68	66	100	0	.	0	.	91	100	9	.	0	.	7	.	0	.	194	54	214	84	0	.	0	.	0	.	0	.	52	100	0	.	63	70	59	.	0	.	17	100	0	.	192	74	75	.	0	.	17	.	158	64	588	44	122	71	111	76	0	.	0	.	16887	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	807	51	1050	53	262	80	734	59	467	75	1064	58	4251	24	3722	25	5923	19	1530	31	711	55	201	.	336	74	0	.	0	.	709	58	709	76	659	58	0	.	1687	37	2620	29	1458	35	2454	28	2853	24	2147	27	2445	25	1610	38	549	60	849	58	0	.	97	.	158	.	590	58	345	73	112	.	364	.	439	71	1006	46	1011	63	877	50	942	51	2016	39	611	53	1495	41	301	73	288	.	0	.	598	59	412	72	1587	38	0	.	1026	46	1280	39	395	53	1120	40	278	80	44	.	0	.	223	.	297	75	0	.	571	51	212	71	218	.	0	.	262	75	240	.	181	70	310	.	0	.	1324	39	335	74	0	.	0	.	0	.	0	.	419	71	0	.	401	84	231	.	0	.	1777	28	0	.	1507	33	205	.	0	.	338	.	744	51	1053	44	624	56	727	59	154	.	0	.	71549	5
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	conifers	46	.	98	70	0	.	0	.	50	.	307	73	2801	20	3774	21	1308	35	677	35	0	.	0	.	143	65	0	.	209	71	868	43	556	49	243	64	0	.	253	83	1458	26	840	37	862	40	3408	20	4127	17	244	47	176	54	820	39	652	50	340	53	1167	27	223	53	387	52	1568	34	226	67	754	44	809	37	26	.	256	68	486	51	1027	41	600	43	0	.	28	.	346	56	108	66	0	.	270	68	356	55	114	71	303	41	68	100	37	83	211	55	396	49	289	47	67	90	11	100	0	.	492	48	329	44	39	88	168	61	108	.	219	75	348	55	439	45	258	61	0	.	0	.	157	58	0	.	0	.	909	30	647	44	270	51	0	.	0	.	48	.	0	.	20	.	3270	20	3444	19	1820	24	186	.	0	.	0	.	209	64	82	.	95	98	19	100	77	67	0	.	48046	5
	broadleaves	90	.	621	51	93	83	0	.	14	.	206	71	481	43	619	29	323	46	291	39	69	.	194	89	171	75	0	.	184	96	281	48	104	67	225	63	0	.	105	59	42	74	115	93	1	100	109	35	0	.	419	44	274	73	97	82	109	55	83	53	211	41	117	73	101	66	152	55	85	100	35	94	81	77	102	.	260	73	295	60	227	47	528	50	83	.	36	.	308	54	155	61	89	.	51	76	4	72	25	71	507	45	248	71	173	60	1	100	64	71	99	100	94	71	363	51	574	43	464	37	135	53	272	47	10	77	0	.	67	96	83	72	172	55	142	68	0	.	0	.	160	76	0	.	0	.	125	49	581	40	142	74	0	.	0	.	63	.	0	.	106	.	4	78	18	64	309	36	0	.	0	.	0	.	188	63	65	.	95	71	142	71	503	50	0	.	14233	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	136	.	719	46	93	83	0	.	65	.	514	69	3283	20	4392	19	1631	32	969	33	69	.	194	89	315	70	0	.	392	60	1149	40	660	45	468	48	0	.	358	71	1500	25	956	36	863	40	3517	20	4127	17	663	43	449	53	917	38	760	48	422	49	1378	27	341	56	487	46	1720	33	311	72	789	43	890	35	128	.	516	51	781	41	1254	38	1128	40	83	.	64	.	654	46	262	63	89	.	322	62	360	55	140	64	810	40	316	72	209	58	212	55	460	45	388	43	161	58	374	49	574	43	956	32	463	42	311	45	178	61	108	.	286	67	430	52	611	39	400	53	0	.	0	.	317	53	0	.	0	.	1034	29	1227	35	412	53	0	.	0	.	111	.	0	.	126	.	3274	20	3462	19	2129	22	186	.	0	.	0	.	397	58	147	.	190	78	161	72	580	51	0	.	62279	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	1447	32	2112	31	768	39	4327	21	2506	29	4083	24	17392	10	14619	11	13839	11	5863	15	356	71	300	63	1067	39	93	.	1028	43	2894	26	4327	23	3755	23	46	63	3959	22	6148	17	3909	18	6606	17	11176	12	11168	11	5754	15	3096	25	5831	18	5681	18	917	39	1598	24	1226	35	1118	35	2878	23	1197	34	2147	30	3042	27	2282	28	1831	37	3133	23	2310	28	4178	22	2174	23	1833	31	1180	31	1430	37	832	28	1708	27	3358	25	2084	28	1617	31	1614	30	1511	26	1021	34	2003	25	1624	28	153	59	35	76	233	85	941	33	644	36	653	47	1115	36	1153	42	1814	30	1163	35	1408	34	1388	31	1450	37	349	90	1808	28	692	45	1016	38	1913	22	908	36	1045	28	1742	29	311	60	800	45	406	54	1047	55	7798	12	5755	14	6853	14	934	37	1066	36	2034	30	2586	26	2438	22	1461	32	1322	38	1344	31	0	.	253772	2
	broadleaves	2964	21	1751	25	866	34	1773	25	255	49	1014	31	3209	19	3799	17	6609	13	3015	16	1121	36	1107	32	1362	27	36	.	473	59	938	31	939	28	1451	30	390	54	1426	26	955	40	601	31	364	49	950	26	171	44	5343	14	1760	27	1009	31	1227	29	559	52	294	35	306	38	230	49	406	33	780	37	131	60	1579	34	1190	34	1441	34	1234	28	910	33	1772	26	2001	19	1440	34	1298	25	979	31	1599	25	606	35	342	74	969	34	855	33	940	33	1394	27	63	56	388	51	259	57	733	27	1577	23	2253	18	1176	27	953	26	1698	23	524	37	56	82	200	55	646	36	428	42	362	41	764	42	78	71	995	29	441	53	359	34	196	35	860	30	1121	28	719	37	674	38	413	44	140	55	274	56	257	36	87	37	1084	23	390	43	898	38	506	41	1331	26	1745	26	763	35	924	32	847	33	0	.	97317	3
	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	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2300497. Birmensdorf, Swiss Federal Research Institute WSL

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