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
number of stems
main tree species · diameter at breast height (main diameter classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
main tree species	diameter at breast height (main diameter classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	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	.
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	.
	12-15 cm	1056	30	100	71	141	55	5566	11	190	50	1231	31	0	.	530	31	8750	9	284	32	2206	22	765	28	258	70	1188	26	1499	20	360	46	175	72	693	26	680	31	1408	20	851	26	2682	17	3943	13	135	48	1324	29	36016	4
	16-23 cm	849	31	279	36	412	40	6682	10	136	77	2338	21	0	.	841	31	10874	9	481	33	1935	19	1029	29	699	58	1515	26	2334	20	449	43	300	42	1386	26	1188	28	2068	19	1417	24	3844	17	5344	13	242	56	1435	28	48076	4
	24-35 cm	694	36	540	34	563	36	7009	10	69	79	2244	21	0	.	836	33	9678	8	474	34	1857	19	1068	27	684	43	1412	24	2006	20	555	38	374	36	1268	23	1146	30	1706	20	1270	24	3576	15	4717	12	264	47	1791	21	45799	4
	36-51 cm	393	22	574	28	508	32	6467	8	104	45	1606	16	0	.	684	23	7313	7	498	28	1480	16	822	18	258	37	631	21	1593	15	259	32	326	30	1108	19	418	26	1227	17	829	26	2529	11	3077	11	206	45	1224	16	34136	3
	=52 cm	315	29	282	34	298	34	3388	9	97	46	690	19	0	.	464	26	3192	8	263	25	850	19	479	19	103	47	322	25	1341	16	139	34	217	37	674	20	228	27	788	17	387	25	1343	13	1400	13	72	47	535	20	17870	3
	total	3308	22	1776	30	1922	28	29112	7	597	40	8108	16	0	.	3355	22	39807	6	2000	22	8328	14	4163	17	2002	45	5068	20	8774	14	1761	30	1391	28	5129	15	3660	21	7198	13	4753	20	13975	11	18481	10	919	39	6309	16	181897	3
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	.
	12-15 cm	203	39	33	71	191	60	2962	12	175	42	504	32	0	.	46	74	210	47	805	25	1201	24	721	25	36	71	272	48	258	36	35	70	497	42	367	37	389	45	234	41	345	33	1291	17	290	35	172	59	222	44	11457	6
	16-23 cm	288	52	33	71	179	55	3472	12	208	44	690	30	0	.	18	100	355	32	1078	22	1548	21	966	22	74	50	265	37	270	26	70	61	483	33	324	38	706	46	320	37	522	34	1355	16	355	29	182	49	121	43	13880	6
	24-35 cm	236	44	66	60	138	56	2834	16	208	39	448	40	0	.	37	71	201	38	590	27	1129	18	620	26	36	100	343	38	356	36	17	100	419	31	336	30	420	33	231	46	383	40	1441	19	363	34	139	50	242	33	11235	6
	36-51 cm	196	35	129	42	285	34	2201	11	77	45	336	25	0	.	83	52	270	27	750	24	910	17	684	18	29	71	150	35	579	19	28	71	212	27	364	28	119	41	168	32	186	43	1083	14	294	34	24	71	296	25	9450	5
	=52 cm	254	29	56	47	131	43	1682	11	28	61	285	31	0	.	57	56	237	25	284	28	545	17	452	22	42	72	98	41	409	20	14	71	85	41	183	27	73	55	143	32	38	61	729	16	125	34	21	100	272	23	6244	5
	total	1178	30	317	40	924	37	13151	9	695	33	2261	25	0	.	242	44	1273	22	3507	18	5333	14	3442	17	215	39	1128	31	1872	17	163	56	1696	28	1573	25	1707	33	1095	32	1475	28	5899	12	1426	25	538	44	1153	19	52265	4
pine	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	.
	12-15 cm	0	.	0	.	0	.	193	60	38	100	0	0	0	.	0	.	1127	27	0	.	0	.	0	0	0	.	87	100	35	100	17	100	19	100	145	77	0	.	49	100	0	.	0	0	1056	32	0	.	59	100	2824	17
	16-23 cm	55	75	0	.	0	.	357	57	17	100	0	.	0	.	0	.	1523	25	20	100	0	.	17	100	0	.	139	100	106	100	51	74	67	100	53	74	0	0	32	100	54	75	0	0	2206	24	0	.	34	71	4731	15
	24-35 cm	36	71	0	.	0	.	484	42	59	58	34	71	86	82	0	.	1100	25	0	0	34	100	86	60	0	.	226	93	123	87	172	68	53	58	35	100	34	71	67	78	149	59	98	69	1402	22	0	.	136	43	4414	13
	36-51 cm	54	55	0	.	9	100	328	38	107	43	128	53	28	79	0	.	627	21	129	52	76	63	55	79	0	.	42	85	29	61	130	42	167	33	27	81	104	40	55	49	73	56	109	56	525	23	0	.	297	23	3098	9
	=52 cm	20	74	0	.	0	.	115	35	21	74	41	53	0	.	0	.	234	24	49	43	13	100	7	100	0	.	7	100	15	71	7	100	13	71	0	.	56	39	21	74	7	100	0	.	72	50	0	.	75	39	774	13
	total	165	48	0	.	9	100	1477	29	243	34	202	43	114	80	0	.	4611	18	197	40	123	58	165	58	0	.	500	95	309	81	378	46	320	36	259	65	194	33	224	69	283	53	206	60	5261	20	0	.	601	23	15841	10
larch	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	.
	12-15 cm	20	100	0	.	0	.	41	71	0	.	0	.	0	.	0	.	1137	18	0	.	17	100	0	.	0	.	35	100	164	56	0	.	0	.	0	.	0	.	1077	24	0	.	420	73	1264	17	0	.	20	100	4193	12
	16-23 cm	19	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	1759	19	0	.	34	71	0	.	0	.	52	100	182	55	17	100	0	.	0	.	67	79	1664	20	0	.	260	45	2313	15	0	.	0	.	6385	9
	24-35 cm	69	79	0	.	0	.	41	71	0	.	52	100	0	.	0	.	2030	15	0	.	0	.	0	.	0	.	0	.	53	74	0	.	0	.	0	.	34	100	1718	18	0	.	176	49	2028	14	0	.	51	58	6253	8
	36-51 cm	34	72	0	.	0	.	71	55	14	71	0	0	0	.	6	100	1803	12	7	100	7	100	0	.	0	.	0	.	73	58	21	74	7	100	0	.	7	100	945	16	0	.	56	43	1801	14	0	.	27	79	4878	7
	=52 cm	43	41	0	.	0	.	35	45	0	.	7	100	0	.	7	100	1252	12	0	.	0	.	7	100	0	.	0	.	29	59	0	.	0	.	14	100	0	.	549	17	0	.	35	72	1360	13	14	100	47	51	3399	7
	total	184	43	0	.	0	.	204	37	14	71	58	89	0	.	13	71	7980	10	7	100	58	66	7	100	0	.	87	100	501	47	38	61	7	100	14	100	109	71	5954	14	0	.	948	43	8767	11	14	100	145	35	25108	6
Arolla pine	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	575	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	273	40	0	.	0	.	880	22
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1182	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	534	27	0	.	0	.	1732	18
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1144	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	576	29	0	.	0	.	1720	18
	36-51 cm	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	436	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	253	29	0	.	0	.	698	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	32	0	.	0	.	249	21
	total	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	3488	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	1734	22	0	.	0	.	5280	14
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	.
	12-15 cm	17	100	0	.	18	100	212	35	0	.	0	.	0	.	0	.	501	36	34	71	15	100	0	.	0	.	0	.	54	58	0	.	86	72	0	.	17	100	0	.	87	100	193	67	70	61	0	.	207	61	1508	19
	16-23 cm	85	60	0	.	18	100	157	61	0	.	0	.	0	.	18	100	194	39	17	100	0	.	0	.	0	.	17	100	74	50	0	.	86	53	18	100	17	100	0	.	0	.	90	54	87	60	0	.	137	59	1015	18
	24-35 cm	69	61	0	.	0	.	154	48	17	100	0	.	0	.	0	.	17	100	0	.	0	.	17	100	0	.	0	.	15	100	17	100	17	100	0	.	0	.	0	.	0	.	69	79	0	0	0	.	17	100	410	27
	36-51 cm	20	74	0	.	0	.	61	50	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	21	100	0	.	0	.	0	.	8	100	0	.	21	100	0	.	0	.	0	.	179	35
	=52 cm	7	100	0	.	0	.	27	61	14	100	0	.	0	.	0	.	0	.	0	.	53	68	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	8	100	0	.	23	100	0	.	0	.	0	.	180	38
	total	198	44	0	.	36	71	610	37	73	100	0	.	0	.	18	100	712	34	51	74	68	57	17	100	0	.	17	100	198	42	38	100	188	53	18	100	34	71	16	100	87	100	396	43	156	53	0	.	360	57	3293	15
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	.
	12-15 cm	815	18	48	73	81	64	3206	13	751	26	484	26	0	.	289	50	516	26	636	28	1048	24	498	27	115	54	223	44	1488	22	525	29	938	24	357	28	370	36	2822	16	132	47	1220	17	579	33	35	71	836	20	18011	5
	16-23 cm	1073	19	54	74	71	61	4685	11	795	29	1362	23	0	.	312	40	733	26	1004	26	1103	23	714	27	312	57	563	32	1898	20	525	31	969	21	617	28	515	26	4399	16	406	39	1874	15	824	32	62	100	944	18	25814	5
	24-35 cm	1152	22	71	71	146	49	4506	11	801	25	1026	24	0	.	248	34	968	26	890	26	1099	21	454	23	262	45	414	37	1749	20	714	30	1439	23	392	23	466	27	2798	16	462	40	2069	14	689	32	73	50	1038	17	23926	5
	36-51 cm	835	17	102	48	148	40	2759	10	691	24	786	22	0	.	244	34	432	27	813	20	667	20	538	19	107	60	271	36	1345	16	443	25	1093	17	314	28	205	25	1009	16	168	38	1637	13	129	51	59	58	743	16	15538	4
	=52 cm	444	21	20	58	21	57	997	14	295	32	133	30	0	.	109	44	158	30	245	26	243	26	65	68	0	.	119	43	427	20	106	36	402	22	161	37	192	30	341	18	49	54	480	17	53	57	24	59	351	22	5437	5
	total	4319	14	295	46	468	35	16154	8	3332	20	3791	19	0	.	1203	30	2807	21	3588	18	4160	17	2269	16	796	41	1591	30	6907	15	2313	23	4840	15	1842	20	1747	18	11369	14	1216	33	7280	11	2274	28	254	47	3913	12	88727	3
maple	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	.
	12-15 cm	221	33	0	.	80	61	869	23	208	35	177	38	0	.	176	52	360	34	142	43	224	42	377	54	68	62	139	88	177	37	52	75	339	39	254	45	189	37	531	35	86	67	1055	22	619	27	21	100	468	32	6831	8
	16-23 cm	257	37	15	100	160	77	1262	19	284	35	430	30	0	.	304	36	274	30	262	37	198	30	426	34	120	48	174	82	455	29	73	77	416	27	247	36	326	37	468	36	153	47	1068	20	588	26	59	76	979	32	8997	7
	24-35 cm	234	30	0	.	223	52	1200	16	242	41	339	39	0	.	186	52	370	37	140	39	359	30	317	42	149	36	218	56	922	21	72	60	162	37	173	32	170	35	131	43	122	43	661	26	175	35	20	100	701	23	7285	7
	36-51 cm	133	28	13	71	33	62	527	18	76	52	69	39	0	.	111	37	119	57	121	29	51	38	79	35	27	59	41	54	380	21	7	100	208	26	89	36	77	46	38	52	35	45	208	23	55	56	37	53	102	29	2636	7
	=52 cm	21	58	0	.	0	.	64	37	30	50	22	58	0	.	13	71	35	51	7	100	10	100	8	100	0	.	0	.	93	36	0	.	21	58	0	.	28	80	0	.	6	100	27	50	0	.	7	100	50	43	442	15
	total	865	21	28	79	496	56	3923	13	840	24	1036	24	0	.	789	31	1159	28	672	24	842	21	1206	31	364	32	571	65	2027	18	204	54	1145	21	763	27	790	27	1168	30	401	42	3019	17	1437	22	144	56	2300	23	26191	5
ash	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	.
	12-15 cm	292	41	0	.	0	.	409	36	72	50	422	40	34	100	50	58	387	33	175	42	345	34	140	47	18	100	40	71	256	36	159	80	138	43	292	46	154	42	759	28	179	40	371	32	238	32	17	100	378	38	5326	9
	16-23 cm	413	41	0	.	107	62	460	24	178	44	239	36	34	100	268	48	440	41	125	42	163	48	125	47	111	62	67	60	572	31	156	69	159	43	393	54	241	38	725	30	123	53	658	24	648	35	35	100	344	36	6781	9
	24-35 cm	134	53	0	.	173	53	661	26	200	39	571	30	17	100	116	47	275	48	232	41	167	39	19	100	198	81	38	71	622	26	17	100	159	43	303	48	314	38	369	35	129	64	555	28	456	51	38	71	273	35	6036	9
	36-51 cm	227	31	0	.	84	55	192	25	50	61	217	33	0	.	100	46	51	51	227	34	256	62	36	65	95	51	14	100	354	24	49	55	129	31	94	49	188	29	94	41	39	69	297	25	56	70	30	71	317	20	3196	9
	=52 cm	88	32	0	.	7	100	107	37	38	44	55	39	0	.	29	59	6	100	62	43	51	64	0	.	36	82	0	.	60	52	14	70	20	58	27	71	65	41	23	71	0	.	117	40	7	100	20	100	102	36	935	12
	total	1154	27	0	.	371	47	1829	19	539	30	1504	24	86	100	563	36	1159	34	820	29	982	28	320	39	458	57	160	43	1864	21	395	61	606	27	1108	38	960	23	1970	26	470	40	1998	19	1405	31	140	53	1414	21	22274	6
oak	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	.
	12-15 cm	0	.	0	.	0	.	32	71	17	100	21	100	17	100	0	.	0	0	0	.	0	.	185	75	0	.	0	.	38	71	0	0	94	84	0	.	17	100	513	34	18	100	546	57	499	37	0	.	34	71	2032	21
	16-23 cm	87	72	0	.	0	.	99	55	59	72	0	0	86	66	0	.	47	75	17	100	42	72	95	100	22	100	0	.	21	100	17	100	128	67	0	.	37	71	769	34	0	.	713	37	378	37	0	.	68	61	2685	16
	24-35 cm	104	70	0	.	0	.	255	40	121	55	94	55	172	66	0	.	47	75	69	61	0	.	19	100	0	.	0	.	19	100	104	47	127	53	0	.	121	38	662	26	18	100	667	33	146	46	0	.	68	50	2813	13
	36-51 cm	74	47	0	.	0	.	122	28	28	71	33	66	139	55	0	.	47	58	90	41	88	49	28	78	0	.	0	.	18	71	56	79	39	56	0	.	137	34	219	31	38	65	251	26	42	63	9	100	102	26	1561	11
	=52 cm	86	41	0	.	0	.	61	33	7	100	14	100	21	100	0	.	27	81	7	100	29	81	7	100	0	.	0	.	8	100	18	100	12	100	6	100	90	57	33	59	8	100	38	55	8	100	0	.	92	31	572	16
	total	350	42	0	.	0	.	569	24	233	48	162	41	435	52	0	.	168	60	184	45	159	48	334	77	22	100	0	.	103	44	195	45	400	59	6	100	402	28	2197	24	83	57	2215	29	1073	31	9	100	365	25	9664	11
sweet chestnut	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2754	22	0	.	17	100	84	100	0	.	0	.	2914	21
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4864	17	0	.	0	.	63	100	0	.	0	.	5027	17
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	453	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2664	16	0	.	0	.	119	71	0	.	0	.	3236	16
	36-51 cm	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	887	19	0	.	7	100	18	71	0	.	0	.	1043	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	456	22	0	.	0	.	44	70	0	.	0	.	545	20
	total	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	781	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11625	14	0	.	24	76	328	75	0	.	0	.	12765	13
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	.
	12-15 cm	467	31	30	100	53	74	2054	16	154	43	253	34	89	73	166	43	2874	23	351	37	427	36	289	32	162	63	181	47	705	29	78	78	171	37	376	33	405	52	5360	12	590	50	1291	23	2213	16	0	.	405	26	19144	6
	16-23 cm	482	25	0	.	68	62	1413	18	168	41	258	37	0	0	283	53	3330	20	317	35	254	36	164	40	38	71	48	73	630	30	58	76	392	44	585	58	213	41	5529	14	617	49	1109	24	2028	17	0	0	370	33	18354	7
	24-35 cm	189	37	0	.	36	100	594	22	111	51	87	45	18	100	34	71	1021	23	126	38	107	58	92	54	36	100	18	100	367	35	17	100	191	41	217	47	190	38	2725	15	52	58	514	34	491	26	17	100	291	29	7542	8
	36-51 cm	97	44	0	.	0	.	261	28	59	54	33	61	0	.	25	100	286	23	103	66	69	52	0	.	7	100	0	.	76	39	7	100	21	57	12	70	114	35	625	18	7	100	202	27	169	36	0	.	180	35	2353	9
	=52 cm	37	52	0	.	8	100	124	55	8	100	62	61	0	.	12	70	53	52	7	100	0	.	0	.	0	.	0	.	34	84	0	.	14	100	6	100	37	100	38	45	8	100	122	50	23	57	0	.	7	100	600	20
	total	1271	22	30	100	165	58	4446	13	500	28	694	28	108	75	520	43	7564	18	905	32	856	27	546	30	243	50	247	42	1811	25	161	55	789	36	1196	37	959	36	14277	11	1274	46	3237	18	4924	15	17	100	1251	21	47992	5
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	.
	12-15 cm	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	16-23 cm	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	24-35 cm	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0
	36-51 cm	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	=52 cm	0	.	0	.	0	.	0	0	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
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	.
	12-15 cm	3090	16	211	44	564	35	15542	7	1606	20	3091	17	141	53	1258	23	16495	7	2428	16	5483	14	2974	15	657	35	2164	20	4675	14	1225	27	2456	19	2483	20	2220	21	15539	7	2289	21	9085	11	11128	8	380	40	3953	14	111136	2
	16-23 cm	3607	15	381	32	1014	30	18604	7	1846	20	5316	14	121	55	2045	21	20810	7	3319	15	5277	12	3536	15	1375	37	2840	19	6542	14	1416	24	2999	15	3622	19	3310	18	20855	7	3291	19	10973	10	15367	8	579	36	4432	14	143478	2
	24-35 cm	2918	16	677	32	1279	26	17737	7	1829	19	4893	14	294	53	1456	24	17304	6	2522	14	4752	12	2693	15	1365	29	2669	19	6231	12	1688	22	2942	16	2723	16	2894	17	13071	6	2587	20	9826	10	11160	8	551	33	4607	12	120669	2
	36-51 cm	2070	12	819	27	1067	23	12999	6	1247	19	3208	12	166	52	1253	20	11509	6	2739	13	3603	12	2242	12	523	28	1149	18	4453	10	1019	20	2201	13	2009	14	1369	16	5275	7	1374	20	6399	8	6418	8	366	35	3288	11	78765	2
	=52 cm	1314	13	358	31	466	28	6601	6	538	23	1308	14	21	100	692	22	5390	6	923	15	1794	13	1025	15	181	37	547	22	2464	12	298	25	784	18	1071	16	768	19	2401	9	503	23	2917	9	3192	9	159	40	1532	13	37248	2
	total	13000	11	2446	28	4390	22	71483	5	7066	16	17817	11	743	46	6704	17	71509	5	11931	12	20910	10	12471	11	4100	28	9369	16	24366	10	5646	19	11382	12	11909	13	10561	14	57141	5	10043	16	39199	7	47265	6	2035	30	17811	9	491296	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. 1302324. Birmensdorf, Swiss Federal Research Institute WSL

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