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
number of stems
development stage · conifers and broadleaves
regional demarcation: forest district (2024)
unit: n/ha
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	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	149	14	20	71	50	.	133	37	70	.	0	.	137	22	346	25	15	87	85	36	0	.	0	.	0	.	0	.	0	.	175	14	132	42	0	.	0	.	182	66	0	.	63	56	150	.	59	48	124	31	0	.	49	72	249	0	410	.	0	.	0	.	0	.	106	67	110	12	0	.	251	34	53	77	0	.	172	.	20	.	0	.	100	.	200	25	0	.	140	.	0	.	0	.	0	.	0	.	0	.	295	4	0	.	70	.	0	.	0	.	70	.	0	.	0	.	0	.	0	.	19	67	0	.	111	70	0	.	0	.	0	.	180	.	250	.	0	.	0	.	0	.	0	.	0	.	70	.	0	.	0	.	0	.	264	.	0	.	354	.	170	.	165	85	190	.	90	67	0	.	209	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	11
	broadleaves	185	48	135	60	70	.	0	.	0	.	0	.	108	49	154	39	5	87	125	30	535	34	240	35	0	.	0	.	0	.	26	90	119	62	0	.	0	.	740	76	0	.	29	43	0	.	77	69	32	68	80	75	132	70	249	0	200	.	602	.	0	.	0	.	0	.	74	24	0	.	12	58	10	77	0	.	548	.	0	.	27	64	390	.	200	0	0	.	290	.	0	.	0	.	16	70	0	.	0	.	50	71	0	.	200	.	0	.	0	.	0	.	0	.	0	.	70	.	70	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	450	.	0	.	0	.	92	.	20	.	37	.	200	.	47	90	100	.	111	36	0	.	0	.	0	.	20	.	0	.	0	.	545	.	0	.	0	.	111	16
	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	334	33	155	62	120	.	133	37	70	.	0	.	244	18	500	27	20	62	211	23	535	34	240	35	0	.	0	.	0	.	202	22	250	36	0	.	0	.	921	48	0	.	92	30	150	.	136	18	156	17	80	75	181	31	498	0	610	.	602	.	0	.	0	.	106	67	183	17	0	.	263	30	63	77	0	.	720	.	20	.	27	64	490	.	400	12	0	.	430	.	0	.	0	.	16	70	0	.	0	.	345	7	0	.	270	.	0	.	0	.	70	.	0	.	0	.	70	.	70	.	19	67	0	.	111	70	0	.	0	.	0	.	180	.	250	.	0	.	0	.	0	.	0	.	0	.	70	.	450	.	0	.	0	.	357	.	20	.	391	.	370	.	212	64	290	.	201	27	0	.	209	57	0	.	20	.	0	.	0	.	545	.	0	.	0	.	228	9
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	55	60	24	51	10	71	0	.	0	.	163	64	190	19	196	29	98	40	53	67	0	.	0	.	0	.	0	.	50	.	112	77	110	58	257	75	0	.	10	90	176	34	0	.	62	69	60	45	73	32	0	.	105	30	410	41	102	40	410	.	7	82	0	.	0	.	0	.	100	.	0	.	0	.	10	70	0	.	137	69	90	.	120	.	8	89	13	82	0	.	0	.	0	.	110	82	42	52	0	.	7	82	0	.	60	74	68	79	121	28	0	.	4	89	0	.	0	.	48	74	0	.	40	82	0	.	0	.	172	40	21	80	57	41	0	.	0	.	100	71	93	39	0	.	0	.	0	.	50	71	0	.	0	.	0	.	100	.	0	.	0	.	79	43	25	33	40	43	82	64	0	.	40	.	60	70	0	.	223	82	0	.	37	20	0	.	78	11
	broadleaves	39	91	70	41	10	71	0	.	0	.	0	.	24	40	157	68	39	63	69	67	0	.	30	71	0	.	0	.	0	.	25	79	75	71	0	.	0	.	20	89	39	74	0	.	67	72	15	78	65	77	0	.	0	.	75	24	106	16	0	.	100	63	0	.	0	.	0	.	0	.	0	.	74	59	0	.	28	.	0	.	90	.	100	.	4	90	17	81	0	.	126	42	0	.	105	60	73	89	0	.	0	.	150	.	0	.	0	.	14	89	0	.	100	36	150	.	250	.	50	0	0	.	17	82	20	.	0	.	0	.	0	.	50	61	0	.	0	.	0	.	68	54	0	.	0	.	0	.	100	71	0	.	0	.	0	.	0	.	0	.	0	.	25	51	50	78	52	56	0	.	0	.	0	.	10	70	25	71	17	82	0	.	80	62	0	.	41	13
	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	94	43	94	41	20	71	0	.	0	.	163	64	215	20	353	14	137	43	121	67	0	.	30	71	0	.	0	.	50	.	137	58	185	6	257	75	0	.	30	60	215	26	0	.	129	45	75	34	138	30	0	.	105	30	485	31	209	13	410	.	107	55	0	.	0	.	0	.	100	.	0	.	74	59	10	70	28	.	137	69	180	.	220	.	12	60	30	42	0	.	126	42	0	.	215	13	115	73	0	.	7	82	150	.	60	74	68	79	136	19	0	.	104	37	150	.	250	.	98	36	0	.	57	50	20	.	0	.	172	40	21	80	107	34	0	.	0	.	100	71	161	38	0	.	0	.	0	.	150	24	0	.	0	.	0	.	100	.	0	.	0	.	104	34	75	49	92	23	82	64	0	.	40	.	70	70	25	71	240	82	0	.	117	48	0	.	118	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	210	29	375	55	423	38	191	87	890	.	1121	20	384	23	510	30	512	21	275	31	0	.	262	65	220	42	660	.	424	73	443	32	447	48	803	55	181	73	498	51	485	27	459	29	12	87	296	27	392	21	409	36	718	54	746	20	1232	18	328	70	1800	.	288	70	0	.	750	.	391	71	656	43	431	69	510	46	484	29	402	27	17	85	153	21	422	37	205	60	0	.	321	32	60	71	170	37	1575	17	338	51	401	68	470	17	1350	.	28	54	0	.	0	.	14	97	0	.	3	98	104	42	65	63	13	96	239	46	295	71	771	26	175	71	0	.	0	.	334	39	0	.	484	64	736	38	70	.	473	15	50	.	260	40	157	77	94	48	594	44	0	.	0	.	575	16	434	20	357	58	433	52	135	18	348	44	178	30	446	19	0	.	0	.	496	28	0	.	331	6
	broadleaves	335	28	343	40	289	27	627	31	450	.	126	15	162	42	171	48	284	22	609	20	0	.	382	27	597	21	20	.	500	0	314	40	353	41	395	73	751	25	151	63	76	91	133	89	840	23	362	22	74	59	243	27	215	53	34	83	33	83	198	71	0	.	571	40	0	.	90	.	387	48	157	69	610	45	129	41	270	65	314	48	935	38	287	29	499	21	151	75	523	3	339	50	735	16	307	36	241	14	540	46	293	51	74	37	21	.	281	29	171	30	232	38	555	22	730	15	785	8	510	28	702	18	687	15	629	21	500	57	21	53	285	71	850	.	0	.	341	25	900	.	330	18	0	.	240	.	50	87	1200	.	420	27	557	42	527	24	216	29	0	.	0	.	124	34	50	52	489	24	203	75	211	30	222	33	404	28	175	44	620	.	486	12	118	50	0	.	363	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	545	12	718	18	712	17	818	17	1340	.	1247	20	546	15	680	21	796	10	884	9	0	.	644	17	817	4	680	.	924	33	757	11	800	20	1198	12	932	14	649	38	561	24	592	36	852	23	658	10	465	15	653	20	933	35	780	16	1265	17	526	17	1800	.	860	3	0	.	840	.	778	28	813	30	1040	21	639	36	754	5	717	35	952	38	440	12	921	10	356	3	523	3	660	12	795	9	477	18	1816	13	878	10	695	28	544	14	1371	.	309	26	171	30	232	38	569	21	730	15	789	8	615	20	767	14	701	13	868	25	795	9	791	24	460	17	850	.	0	.	675	29	900	.	815	31	736	38	310	.	522	21	1250	.	680	14	713	16	621	20	809	34	0	.	0	.	699	12	483	17	846	19	636	17	346	11	570	22	582	16	620	8	620	.	486	12	614	13	0	.	694	3
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	191	45	260	67	58	42	402	24	907	24	548	32	464	13	367	20	327	30	402	19	20	.	0	.	161	34	0	.	20	.	635	47	387	36	518	20	0	.	429	34	192	66	702	9	530	.	853	21	557	14	378	25	180	39	397	25	502	15	110	39	140	.	420	.	373	51	456	23	282	71	0	.	236	38	159	27	0	.	451	42	0	.	445	36	349	27	24	72	338	35	261	69	186	20	296	4	550	25	267	44	430	.	301	69	81	8	0	.	418	35	480	75	0	.	3	93	8	56	155	76	47	78	123	87	540	8	1030	.	639	22	70	46	291	55	480	28	252	24	421	59	221	17	750	.	255	54	425	13	106	64	194	26	284	27	24	81	420	.	170	.	100	.	374	12	407	23	421	26	0	.	313	53	260	65	291	35	283	32	428	15	605	1	0	.	0	.	351	5
	broadleaves	320	17	454	34	379	14	208	34	211	65	257	23	102	35	129	33	368	21	371	18	220	.	585	.	396	15	0	.	660	.	196	58	217	23	217	25	0	.	293	30	412	40	54	64	0	.	148	87	0	.	364	21	459	19	171	42	198	42	110	71	600	.	300	.	101	35	224	33	406	28	0	.	394	31	377	30	1065	5	200	64	0	.	358	32	356	25	723	50	230	35	166	64	399	12	0	.	30	86	303	44	0	.	276	46	275	6	0	.	108	70	323	67	416	19	599	16	553	23	361	43	248	22	614	28	0	.	0	.	193	90	508	24	414	57	468	21	206	41	72	71	375	24	120	.	204	12	112	29	145	14	288	13	166	38	453	11	210	.	60	.	40	.	0	.	32	51	122	53	0	.	571	30	195	56	248	25	156	28	152	46	225	55	0	.	0	.	263	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	510	11	714	14	437	6	610	12	1117	7	806	26	566	10	497	16	695	9	773	11	240	.	585	.	557	17	0	.	680	.	831	30	604	17	735	11	0	.	722	18	604	29	756	4	530	.	1002	11	557	14	742	8	639	3	568	20	700	14	220	16	740	.	720	.	473	48	680	14	689	17	0	.	629	14	536	17	1065	5	652	32	0	.	803	15	705	19	747	46	568	21	428	17	585	13	296	4	580	22	570	18	430	.	577	14	356	3	0	.	527	15	803	18	416	19	602	17	561	23	516	11	295	31	737	15	540	8	1030	.	831	10	578	17	704	31	947	17	459	15	493	40	596	15	870	.	459	31	538	10	251	19	482	5	451	19	477	14	630	.	230	.	140	.	374	12	439	19	543	17	0	.	884	6	455	13	539	14	439	16	580	22	830	15	0	.	0	.	614	2
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	65	58	165	26	164	44	223	17	510	6	396	15	415	11	390	8	256	11	337	13	76	55	53	43	203	55	0	.	309	21	520	0	360	24	420	10	50	.	221	39	312	17	317	20	465	14	457	11	440	17	196	15	227	23	455	9	334	16	322	60	200	.	290	19	410	.	158	18	223	26	367	19	458	37	386	15	273	44	179	23	287	40	279	20	116	58	218	12	156	53	151	31	98	30	183	29	590	11	233	37	239	18	166	22	138	20	238	31	221	29	338	17	150	.	0	.	0	.	270	34	127	85	92	82	210	71	455	25	423	26	293	45	215	36	708	58	156	42	0	.	193	27	438	6	300	23	248	39	130	.	240	.	357	28	220	.	0	.	150	.	582	25	347	22	409	28	355	15	183	29	113	25	283	18	298	25	181	12	185	41	220	49	289	28	0	.	305	3
	broadleaves	314	18	193	24	124	64	200	24	69	32	136	52	103	28	175	17	167	13	237	16	270	20	389	63	123	15	0	.	24	63	23	77	140	48	70	44	509	.	197	32	38	67	136	33	4	97	21	70	25	64	243	17	135	25	90	41	202	23	201	62	150	.	137	43	220	.	10	74	342	21	85	79	300	38	148	79	302	49	239	25	176	26	239	31	320	29	211	36	210	25	190	38	176	26	210	49	28	64	140	57	181	26	141	15	262	26	0	.	52	73	19	69	120	.	417	8	0	.	0	.	309	19	415	37	247	33	0	.	16	43	105	37	98	62	60	83	274	46	0	.	178	47	208	10	138	23	101	36	320	.	200	.	65	39	220	.	497	.	290	.	122	66	7	93	0	.	25	51	100	35	130	23	97	43	97	42	219	33	262	25	226	21	127	9	0	.	146	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	380	11	358	17	287	23	424	7	580	7	531	11	518	9	565	6	423	5	574	9	347	27	442	60	327	31	0	.	333	24	543	3	500	11	490	10	559	.	417	25	350	15	453	15	469	14	479	11	465	16	439	10	363	8	545	7	536	12	523	32	350	.	427	3	630	.	168	21	565	6	452	8	757	17	534	18	575	14	418	17	463	25	519	19	436	17	429	15	366	24	342	18	275	15	393	23	618	11	373	10	419	12	307	7	399	21	238	31	273	17	357	19	270	.	417	8	0	.	270	34	436	29	507	29	457	17	455	25	439	26	398	23	313	5	768	60	429	23	0	.	371	21	645	1	438	11	349	36	450	.	440	.	422	23	440	.	497	.	440	.	704	14	353	21	409	28	379	13	283	18	243	12	379	12	395	18	400	18	447	19	446	32	416	19	0	.	450	2
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	15	57	236	35	103	72	190	40	282	37	336	19	282	18	195	15	161	15	198	19	47	86	230	.	55	45	0	.	0	.	127	22	654	35	64	32	0	.	235	32	224	19	235	25	233	14	203	15	239	14	148	17	204	33	253	39	382	17	0	.	80	.	140	.	191	15	230	9	379	.	450	.	217	13	185	11	417	49	212	23	160	15	311	15	88	48	139	30	85	4	330	.	0	.	230	24	177	72	245	16	0	.	257	28	125	27	89	12	301	29	150	39	0	.	0	.	180	.	27	81	0	.	107	59	242	9	310	.	0	.	174	40	380	.	95	11	140	.	0	.	305	14	383	43	0	.	0	.	0	.	0	.	405	14	0	.	185	63	180	.	0	.	169	14	0	.	248	21	240	.	0	.	462	.	174	24	68	51	160	33	191	12	80	.	0	.	205	4
	broadleaves	213	13	108	41	193	51	86	74	9	76	115	40	93	42	173	31	160	14	149	26	104	38	70	.	195	5	0	.	0	.	223	37	12	58	309	32	0	.	125	55	76	40	68	41	6	67	16	98	9	67	120	20	173	22	87	38	34	45	0	.	20	.	120	.	354	73	30	71	0	.	0	.	74	47	176	45	79	49	84	80	232	45	51	60	247	11	169	28	93	38	50	.	0	.	66	60	191	51	175	36	0	.	156	28	285	31	89	71	42	93	0	.	390	.	0	.	70	.	32	60	0	.	239	58	189	75	0	.	0	.	75	75	100	.	0	.	20	.	0	.	106	40	245	41	0	.	0	.	0	.	0	.	85	66	0	.	85	13	120	.	0	.	26	97	0	.	97	63	320	.	0	.	73	.	82	44	166	25	164	51	78	75	0	.	0	.	110	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	228	15	344	14	296	8	276	13	291	34	451	7	376	12	368	12	321	9	347	12	151	17	300	.	250	6	0	.	0	.	349	30	666	33	373	30	0	.	360	23	300	11	303	16	238	14	218	14	248	14	268	11	377	19	341	24	416	19	0	.	100	.	260	.	545	46	260	16	379	.	450	.	291	2	361	20	496	47	296	29	391	22	361	14	335	12	308	12	179	22	380	.	0	.	295	31	369	8	420	20	0	.	413	17	411	27	179	35	343	22	150	39	390	.	0	.	250	.	59	43	0	.	347	33	432	28	310	.	0	.	249	51	480	.	95	11	160	.	0	.	410	15	628	42	0	.	0	.	0	.	0	.	490	23	0	.	270	40	300	.	0	.	195	14	0	.	345	18	560	.	0	.	535	.	255	14	233	17	325	14	269	21	80	.	0	.	315	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	170	.	48	49	0	.	0	.	390	.	294	23	307	10	280	13	290	18	230	19	0	.	0	.	79	18	0	.	425	43	455	27	225	31	215	40	0	.	163	56	318	13	358	19	386	20	336	13	425	9	172	24	261	47	338	19	359	30	134	22	301	18	308	31	226	30	364	17	419	13	383	32	328	24	170	.	170	57	240	30	363	35	298	55	0	.	350	.	378	46	70	30	0	.	198	36	300	15	165	16	179	31	129	71	121	49	203	35	280	30	225	25	89	71	3	93	0	.	118	38	86	33	65	78	341	32	150	.	457	12	169	33	139	26	170	47	0	.	0	.	155	33	0	.	0	.	214	22	124	25	179	14	0	.	0	.	210	.	0	.	33	.	383	12	445	11	329	16	290	.	0	.	0	.	401	57	260	.	154	25	10	72	27	35	0	.	283	3
	broadleaves	130	.	346	29	56	30	0	.	70	.	312	46	111	31	129	21	141	26	195	31	270	.	450	31	439	49	0	.	415	61	230	31	140	43	350	26	0	.	327	24	20	65	58	66	5	95	32	32	0	.	382	26	191	57	85	45	164	37	91	41	135	27	122	40	113	38	62	43	177	79	38	74	41	54	920	.	392	47	140	26	172	28	386	34	310	.	90	.	413	31	433	22	510	.	67	21	35	67	125	59	363	21	534	18	230	18	13	86	70	60	130	92	169	43	494	26	382	26	212	33	181	35	340	25	57	67	0	.	148	67	25	43	145	47	233	43	0	.	0	.	224	45	0	.	0	.	87	26	320	28	151	52	0	.	0	.	150	.	0	.	537	.	7	42	11	56	138	25	0	.	0	.	0	.	308	13	800	.	252	38	399	7	461	10	0	.	137	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	300	.	395	24	56	30	0	.	460	.	606	19	418	9	409	8	432	7	424	16	270	.	450	31	518	43	0	.	840	15	685	14	365	18	565	11	0	.	490	3	338	11	416	14	392	19	368	11	425	9	554	16	453	27	423	13	523	24	225	15	436	14	430	14	339	21	426	12	596	15	421	29	369	18	1090	.	561	28	380	18	535	20	684	25	310	.	440	.	791	9	503	22	510	.	265	23	335	17	291	25	543	9	664	28	351	21	215	38	350	25	356	27	258	19	497	26	382	26	330	22	267	29	405	22	398	28	150	.	605	13	194	25	283	24	403	26	0	.	0	.	380	21	0	.	0	.	300	19	445	17	330	25	0	.	0	.	360	.	0	.	570	.	389	12	456	11	468	11	290	.	0	.	0	.	709	38	1060	.	406	33	409	9	488	9	0	.	420	2
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	122	22	170	26	174	35	262	16	510	14	455	17	343	6	327	7	269	10	282	10	40	52	110	59	162	26	660	.	314	28	392	19	317	18	398	15	155	70	266	20	293	9	311	12	285	12	320	9	383	7	249	14	250	21	418	8	421	12	208	30	314	28	295	18	234	20	348	13	318	31	426	20	306	22	265	21	232	26	257	16	210	28	289	17	241	23	156	17	219	28	179	23	142	19	175	16	478	20	246	15	231	26	253	18	154	32	122	24	220	18	259	19	26	52	2	68	8	64	111	24	72	31	63	38	259	21	433	26	549	15	138	24	161	21	340	31	205	24	150	67	233	15	550	21	236	28	275	14	103	22	227	24	300	18	104	33	312	37	211	19	386	33	354	9	393	9	303	11	236	27	162	28	294	19	232	18	231	17	204	23	175	32	211	27	0	.	273	2
	broadleaves	252	13	249	17	184	23	221	20	97	36	180	21	102	15	155	11	191	9	284	11	250	24	320	21	341	17	20	.	270	37	182	23	178	21	207	19	704	22	206	22	67	29	85	26	75	43	101	22	17	36	247	11	179	17	104	21	162	19	162	32	142	25	215	30	155	44	89	28	298	25	66	46	232	26	224	26	372	25	174	21	286	31	259	18	316	15	206	28	284	16	243	23	381	13	165	28	69	32	232	27	222	22	182	19	196	19	85	41	76	27	97	51	380	19	587	11	628	9	281	22	373	19	463	14	248	30	126	83	80	59	200	31	179	33	204	30	235	28	150	79	208	18	147	34	160	16	81	19	321	23	322	20	186	32	436	19	170	26	131	38	177	41	40	30	26	30	137	18	116	40	204	31	135	26	206	20	190	19	184	24	293	18	182	22	0	.	183	2
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	374	9	419	12	358	19	484	9	606	15	635	14	445	5	481	5	460	5	566	7	291	21	430	18	504	13	680	.	584	19	574	12	495	11	605	9	859	15	473	13	360	8	396	10	359	11	421	7	400	6	496	7	429	12	522	6	584	9	370	15	456	19	510	11	388	21	437	11	616	14	492	17	538	13	489	14	604	13	431	14	496	18	548	11	557	12	362	15	503	12	421	12	524	11	340	13	547	19	478	12	452	13	436	10	350	17	207	18	296	12	356	15	406	17	588	11	636	9	392	15	445	16	526	11	507	18	560	19	629	13	338	17	340	18	544	24	440	18	299	45	442	11	696	16	396	17	356	13	425	16	549	11	487	12	539	14	482	27	342	12	562	17	394	8	419	8	439	8	352	17	366	20	429	12	439	12	421	11	389	15	468	13	393	14	0	.	456	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. 2283299. Birmensdorf, Swiss Federal Research Institute WSL

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