Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
total wood volume (stemwood)
tree species (5 classes) · tree state (living/dead)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
tree species (5 classes)	tree state (living/dead)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
spruce	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	.
	living	347	30	729	30	1244	28	1286	26	1834	25	3084	20	16095	8	7480	11	5145	13	2995	14	107	52	281	44	324	39	0	.	771	34	2561	21	3188	18	1787	22	19	100	3677	17	7177	12	4229	14	7679	11	11402	9	6226	11	3237	15	1556	22	1530	22	4004	17	393	37	1137	21	679	38	529	31	2109	21	1057	27	1971	22	2539	18	1272	25	1280	27	2951	19	1618	26	3554	19	1646	21	897	31	573	35	375	38	402	32	2329	21	2248	20	1581	24	1183	33	755	33	1038	28	893	27	2017	19	1932	22	7	100	138	76	13	100	450	35	48	60	384	33	779	31	1259	30	1610	26	1167	31	1672	23	2613	20	700	31	129	44	1454	26	145	41	333	37	2245	17	1192	28	971	23	1043	25	206	51	600	41	151	51	577	37	4643	12	4370	14	5853	12	1028	26	1225	29	651	28	1075	24	1349	25	697	40	653	35	902	31	0	.	181288	2
	dead	45	58	56	42	33	74	41	42	73	39	184	39	2189	13	451	21	122	31	203	25	11	84	16	68	47	52	0	.	64	54	194	32	714	37	118	32	0	.	380	26	345	24	393	23	775	20	1075	17	376	19	138	27	168	35	115	38	610	28	16	64	17	94	20	57	0	.	41	47	81	41	240	33	173	34	59	47	100	46	445	27	107	46	169	47	54	34	27	78	32	52	18	62	37	48	110	37	292	36	129	43	16	63	40	61	7	54	76	54	184	28	101	38	0	.	15	80	0	.	46	51	12	93	66	82	83	50	90	51	219	32	110	43	132	33	221	32	107	43	20	91	178	49	22	100	17	61	82	41	47	59	44	49	139	48	13	100	36	94	29	100	34	64	753	17	392	22	862	18	61	49	64	54	65	38	64	51	36	57	29	58	6	79	41	45	0	.	15662	4
	total	391	29	785	28	1277	27	1327	25	1907	24	3268	20	18284	8	7931	11	5267	13	3198	14	117	48	297	43	371	35	0	.	835	33	2756	20	3902	17	1905	21	19	100	4057	17	7523	12	4622	14	8454	11	12477	9	6602	11	3375	14	1724	21	1645	21	4614	16	409	37	1154	21	699	38	529	31	2151	21	1138	27	2211	21	2712	18	1331	24	1380	26	3397	17	1725	25	3724	18	1700	21	924	30	605	35	393	37	439	30	2439	20	2539	19	1711	24	1199	33	795	32	1044	28	969	26	2202	19	2033	22	7	100	153	71	13	100	496	34	60	63	450	34	862	30	1349	30	1829	25	1277	30	1804	22	2834	20	807	30	149	41	1632	24	167	41	350	36	2327	16	1240	28	1016	23	1182	25	219	49	636	41	180	47	611	38	5396	12	4762	14	6715	12	1089	26	1289	28	716	27	1138	23	1385	24	726	39	659	34	943	30	0	.	196949	2
fir	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	.
	living	524	37	192	58	328	39	1134	28	521	30	1425	27	2226	22	7446	11	4165	14	3253	18	85	82	305	55	304	40	0	.	181	60	248	45	1262	24	1001	32	0	.	635	34	302	41	822	27	300	45	739	29	10	100	3458	13	1353	24	2609	18	1628	23	415	42	1147	25	427	32	511	34	1727	23	235	45	162	42	653	33	866	25	364	37	503	33	575	33	1977	23	114	47	534	32	519	34	136	43	450	31	731	31	678	30	539	30	566	37	235	39	277	58	6	93	282	54	93	93	0	.	0	.	14	100	320	38	379	37	263	51	419	37	15	91	254	41	585	35	289	50	990	25	199	45	37	88	635	32	420	45	485	32	427	38	438	35	422	27	409	38	202	69	295	44	373	53	190	48	361	52	354	40	808	24	550	29	226	55	392	42	902	25	536	34	76	68	13	100	214	60	0	.	63668	3
	dead	35	65	0	.	0	.	69	50	6	100	78	56	119	31	283	25	157	30	146	37	11	100	0	.	19	67	0	.	3	72	5	71	95	43	56	50	0	.	40	68	37	57	39	55	34	74	49	48	10	100	254	27	16	64	104	38	198	36	18	63	12	74	12	87	0	.	14	59	21	95	35	59	15	54	80	55	13	92	29	62	24	74	15	70	0	.	17	94	18	63	0	.	86	48	39	47	29	54	29	73	12	87	0	.	0	.	0	.	29	62	9	100	0	.	0	.	0	.	11	100	92	99	13	90	24	49	0	.	29	58	27	67	56	81	90	61	84	55	0	.	30	75	17	93	22	75	20	63	20	63	22	62	1	100	9	76	0	.	9	87	6	100	12	56	34	54	126	38	50	50	0	.	7	100	68	46	41	65	2	100	0	.	0	.	0	.	3342	7
	total	559	37	192	58	328	39	1202	27	527	31	1503	27	2345	21	7730	11	4322	13	3399	18	96	75	305	55	323	40	0	.	183	60	253	44	1357	23	1057	32	0	.	675	34	340	39	861	27	334	47	788	28	20	71	3712	13	1369	24	2713	17	1826	22	433	41	1159	25	439	31	511	34	1740	23	256	47	198	39	668	33	946	24	376	37	532	32	599	32	1992	23	114	47	551	31	537	33	136	43	536	28	770	30	707	29	568	29	578	36	235	39	277	58	6	93	311	50	102	93	0	.	0	.	14	100	331	38	471	37	276	52	443	36	15	91	284	41	612	35	345	51	1080	26	283	41	37	88	665	31	437	46	507	32	447	38	458	34	444	27	411	38	210	67	295	44	381	52	196	47	373	51	388	39	934	23	600	28	226	55	399	41	970	24	577	33	78	67	13	100	214	60	0	.	67010	3
other conifers	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	.
	living	251	39	225	40	165	41	372	34	36	75	81	83	569	35	53	50	773	21	363	40	227	52	101	72	210	35	101	.	78	74	129	66	0	.	194	54	62	79	41	84	648	25	1199	19	280	35	3270	12	7876	9	513	29	184	73	228	53	61	68	85	75	31	71	23	84	24	100	14	100	6	100	126	75	85	61	298	65	43	73	570	34	33	80	55	70	399	31	122	45	157	41	56	48	228	37	139	57	5	88	63	65	96	40	131	44	290	37	498	31	825	23	496	27	288	32	23	60	124	68	1406	17	390	29	438	40	40	99	29	100	151	48	120	39	37	65	59	80	44	97	211	59	91	85	44	62	28	64	0	.	65	58	0	.	276	49	1	100	69	91	51	100	23	74	7241	10	3302	12	2462	14	35	92	54	52	221	37	149	35	311	31	191	36	292	40	193	50	0	.	41648	3
	dead	18	66	3	74	5	100	11	90	1	100	0	.	92	34	5	100	76	52	19	54	25	100	0	.	25	57	0	.	1	100	66	59	0	.	38	58	11	100	0	.	40	48	89	41	17	58	346	21	451	17	65	41	0	.	1	100	15	78	15	100	1	100	17	100	0	.	15	100	0	.	4	100	5	85	0	.	0	.	36	43	0	.	2	100	3	100	0	.	10	58	26	100	47	48	37	56	0	.	1	100	0	.	0	.	2	100	12	68	72	34	35	47	31	73	36	75	2	100	62	37	10	79	17	53	2	100	0	.	19	100	19	64	0	.	0	.	0	.	6	86	3	51	0	.	5	100	0	.	7	100	0	.	11	100	0	.	0	.	0	.	0	.	663	15	267	21	344	18	0	.	5	100	23	59	9	75	2	100	0	.	6	100	0	.	0	.	3312	6
	total	269	37	228	40	170	40	383	34	36	75	81	83	661	31	58	46	849	21	382	39	252	55	101	72	235	35	101	.	79	73	195	57	0	.	232	53	72	72	41	84	688	25	1288	19	297	34	3616	12	8327	9	578	28	184	73	229	53	76	60	101	71	33	71	39	90	24	100	30	100	6	100	130	74	90	59	298	65	43	73	606	33	33	80	57	68	402	31	122	45	167	40	83	50	275	35	177	55	5	88	64	64	96	40	131	44	293	37	510	31	898	23	531	26	319	34	59	56	126	69	1468	17	400	29	455	40	42	99	29	100	170	49	139	35	37	65	59	80	44	97	218	58	94	83	44	62	33	69	0	.	72	55	0	.	287	51	1	100	69	91	51	100	23	74	7904	9	3569	12	2806	13	35	92	59	53	244	34	157	34	313	31	191	36	298	40	193	50	0	.	44961	3
beech	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	.
	living	1782	18	1014	24	706	25	1883	23	279	34	953	30	2910	20	2728	14	4112	11	3379	12	443	42	920	27	2043	21	0	.	634	44	796	30	1181	24	1411	25	0	.	1093	22	562	35	495	28	53	68	562	30	0	.	4303	12	1347	23	715	28	1064	26	440	34	329	36	378	36	255	32	437	26	427	30	154	63	1185	28	675	28	779	34	862	23	1181	23	1471	20	2057	16	1476	21	1089	21	1065	25	1817	19	729	24	227	60	860	29	491	30	299	36	835	25	0	.	264	41	0	.	1040	30	971	26	649	26	1122	21	917	26	793	26	419	40	14	100	131	59	507	37	164	41	467	31	639	32	54	52	1082	27	294	46	107	35	307	30	343	31	1160	25	590	34	463	38	273	42	203	43	343	40	8	100	38	74	879	22	355	37	634	33	823	36	917	25	887	22	713	35	397	35	476	30	0	.	76329	2
	dead	63	67	43	47	46	96	19	61	0	100	19	55	61	33	101	34	128	34	126	44	37	100	55	58	67	38	0	.	21	60	23	69	19	71	30	52	0	.	19	44	8	51	18	60	0	.	4	74	0	.	80	30	31	50	1	71	91	63	2	100	4	81	1	100	0	.	6	51	25	52	1	100	39	45	21	100	35	49	68	44	24	57	54	52	20	66	14	71	56	76	24	59	44	45	51	66	10	71	5	76	5	83	0	.	5	73	0	.	12	100	0	.	33	44	25	50	20	52	44	39	20	68	63	50	1	72	0	.	0	.	11	64	1	100	8	80	17	90	7	92	37	47	6	77	0	.	1	100	13	73	11	56	13	83	12	82	0	.	0	.	36	71	0	.	1	100	22	45	10	93	12	100	8	91	11	59	17	60	5	78	23	100	14	63	0	.	2136	8
	total	1846	18	1056	23	752	26	1902	23	279	34	972	29	2971	20	2829	13	4240	11	3505	12	480	40	975	27	2110	21	0	.	655	43	819	30	1200	24	1441	25	0	.	1112	22	570	35	513	28	53	68	566	30	0	.	4383	12	1379	23	716	28	1155	25	443	34	333	36	379	36	255	32	443	26	452	29	154	63	1224	28	696	28	814	34	931	22	1205	23	1525	20	2076	16	1490	21	1145	21	1089	25	1861	19	780	25	237	58	864	29	497	29	299	36	840	24	0	.	276	43	0	.	1073	29	996	26	669	26	1166	21	937	26	855	26	419	40	14	100	131	59	517	36	165	41	475	31	656	32	61	48	1119	27	299	45	107	35	308	30	356	31	1172	24	603	34	475	38	273	42	203	43	379	40	8	100	38	74	901	22	365	37	647	33	831	36	927	24	904	22	718	35	421	33	490	30	0	.	78465	2
other broadleaves	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	.
	living	1639	22	792	23	560	23	744	23	161	45	498	31	1710	15	939	20	2145	14	916	20	525	32	500	36	560	25	0	.	287	35	530	41	653	23	930	24	458	41	841	22	274	26	327	38	657	23	1285	18	494	32	2291	13	776	45	485	31	422	23	224	35	148	33	142	48	212	64	99	41	497	37	117	38	428	35	822	25	431	27	530	23	650	28	774	22	660	25	342	29	303	26	399	30	632	24	608	29	227	39	352	37	955	27	955	30	925	34	291	32	792	22	495	30	1023	23	1668	21	2637	14	1619	20	1062	23	1682	20	259	37	157	46	86	48	507	36	566	36	470	25	441	32	149	46	306	31	835	32	301	44	60	37	443	32	446	34	537	34	287	34	283	45	18	73	430	46	578	21	554	21	1331	15	349	31	590	30	400	30	575	23	574	22	399	32	510	30	579	29	0	.	58121	3
	dead	74	38	47	79	6	89	36	43	14	49	24	50	148	24	26	46	65	32	16	39	29	60	15	68	20	53	0	.	27	46	13	73	14	43	42	46	40	61	49	39	24	40	24	49	85	35	166	25	19	53	143	22	44	47	28	48	51	42	8	42	3	72	4	75	1	100	5	100	17	91	25	53	14	64	19	83	40	44	49	34	22	49	21	52	28	54	3	100	1	100	19	56	24	44	70	47	4	100	43	54	11	52	18	80	16	100	77	48	76	34	75	38	93	34	253	31	411	19	200	26	118	34	213	24	7	63	7	82	5	62	33	42	38	39	23	35	11	73	10	83	12	74	21	59	16	62	3	76	37	79	27	67	37	44	9	75	32	65	1	100	7	74	86	36	60	44	135	26	11	68	5	100	49	42	83	53	29	45	9	66	9	100	20	92	0	.	4100	5
	total	1714	22	839	24	565	23	780	23	175	43	522	30	1857	15	965	20	2210	14	932	20	554	32	515	37	581	24	0	.	314	34	543	40	667	23	972	24	498	42	890	22	298	25	351	36	742	22	1451	17	513	32	2434	13	820	43	513	30	473	23	232	35	152	33	145	47	213	64	104	39	514	37	142	38	443	35	840	25	471	26	578	22	672	28	795	22	688	25	345	28	304	26	418	30	656	24	678	28	231	39	395	35	967	27	972	29	941	34	368	31	868	22	570	30	1116	23	1921	20	3048	13	1819	19	1180	23	1895	19	266	37	164	47	91	47	540	35	604	35	493	25	452	32	159	44	318	30	856	32	317	45	63	36	480	33	473	34	574	33	297	34	315	45	19	69	437	46	664	21	614	20	1466	15	360	31	596	30	449	30	658	23	603	21	408	32	519	30	599	30	0	.	62221	2
indeterminable	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	.
	living	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead	9	52	0	.	0	.	0	.	0	.	7	85	113	29	4	84	59	46	6	55	2	100	0	.	7	100	0	.	1	100	0	100	0	.	5	100	0	.	9	74	20	52	27	65	65	62	25	51	20	69	12	88	2	100	2	100	20	64	5	100	0	.	0	.	0	.	5	100	0	.	9	87	4	100	6	100	2	73	32	59	0	.	3	100	0	.	1	100	20	86	0	.	8	73	19	59	0	.	0	.	0	.	1	100	0	.	3	100	0	.	0	.	0	100	2	100	9	71	10	62	7	85	0	100	12	100	0	.	10	80	26	54	2	84	2	61	2	100	0	.	5	100	0	.	1	100	0	.	0	.	0	.	0	.	1	100	3	100	0	.	8	100	40	46	29	52	23	43	4	100	0	.	0	.	0	.	7	100	11	100	0	.	1	100	0	.	752	11
	total	9	52	0	.	0	.	0	.	0	.	7	85	113	29	4	84	59	46	6	55	2	100	0	.	7	100	0	.	1	100	0	100	0	.	5	100	0	.	9	74	20	52	27	65	65	62	25	51	20	69	12	88	2	100	2	100	20	64	5	100	0	.	0	.	0	.	5	100	0	.	9	87	4	100	6	100	2	73	32	59	0	.	3	100	0	.	1	100	20	86	0	.	8	73	19	59	0	.	0	.	0	.	1	100	0	.	3	100	0	.	0	.	0	100	2	100	9	71	10	62	7	85	0	100	12	100	0	.	10	80	26	54	2	84	2	61	2	100	0	.	5	100	0	.	1	100	0	.	0	.	0	.	0	.	1	100	3	100	0	.	8	100	40	46	29	52	23	43	4	100	0	.	0	.	0	.	7	100	11	100	0	.	1	100	0	.	752	11
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	.
	living	4543	14	2952	17	3002	17	5418	14	2832	22	6041	17	23510	7	18647	8	16340	8	10906	10	1387	26	2107	22	3441	18	101	.	1951	25	4265	17	6285	14	5324	16	539	39	6288	14	8964	11	7070	12	8969	10	17257	7	14606	8	13802	8	5216	16	5567	15	7180	14	1558	23	2793	18	1648	26	1531	25	4387	17	2223	21	2530	20	4890	16	3934	18	2897	19	5416	15	4057	18	7831	14	4876	14	3370	17	2641	18	2031	20	3528	16	4536	16	3385	18	3395	19	3292	20	2375	23	3364	18	1687	20	4181	14	3016	17	2359	17	2799	17	3437	12	4916	11	2797	16	3560	15	1916	23	1474	27	2233	22	2885	20	2729	19	4599	17	2024	24	580	38	3569	19	1738	27	1254	25	3039	16	2480	20	3000	17	2855	18	1159	29	1520	28	796	42	1563	27	12832	8	8618	10	11333	9	2317	21	2730	21	2487	20	3617	17	3657	17	2077	23	1866	24	2363	21	0	.	421054	1
	dead	244	28	149	34	90	57	175	25	93	32	312	30	2721	11	870	15	607	16	516	21	115	44	85	41	187	29	0	.	118	44	302	29	842	32	289	25	50	52	496	22	475	20	591	19	975	17	1664	12	875	14	693	15	261	26	251	27	984	22	64	41	37	50	53	47	1	100	86	33	143	30	315	29	251	27	185	34	190	30	659	22	177	34	264	33	105	28	63	46	138	38	88	44	247	25	327	29	336	32	207	31	45	43	58	49	29	57	168	34	374	20	219	25	157	26	331	29	443	18	373	19	258	39	373	23	129	37	97	48	282	29	226	30	229	35	345	31	219	40	43	50	266	36	66	52	60	41	106	38	126	38	105	32	202	36	44	45	71	56	39	77	90	46	1554	12	782	15	1512	13	135	39	87	45	152	26	233	31	132	33	55	43	45	59	76	41	0	.	29304	3
	total	4787	14	3100	17	3092	17	5594	14	2925	21	6352	17	26231	7	19517	8	16947	8	11422	10	1502	25	2192	22	3628	18	101	.	2068	24	4566	17	7127	14	5613	16	589	40	6784	14	9439	11	7662	12	9944	10	18922	7	15481	8	14494	8	5478	16	5818	15	8164	13	1622	23	2830	18	1702	27	1532	25	4473	17	2366	21	2845	19	5141	15	4119	18	3087	19	6075	14	4233	17	8095	14	4981	14	3433	17	2778	18	2119	20	3775	16	4863	16	3721	17	3602	19	3337	20	2433	22	3394	18	1856	19	4555	14	3236	16	2516	17	3131	16	3880	12	5288	11	3055	15	3932	14	2045	23	1571	27	2515	22	3111	20	2958	19	4944	17	2243	24	623	36	3834	18	1804	26	1314	25	3145	16	2606	20	3105	17	3056	18	1202	29	1592	28	835	41	1654	27	14387	8	9400	10	12844	9	2453	20	2817	21	2638	20	3850	17	3789	16	2132	23	1911	23	2439	21	0	.	450357	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. 2291301. Birmensdorf, Swiss Federal Research Institute WSL

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