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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI2
growing stock (stemwood)
main tree species · diameter at breast height (main diameter classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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 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	.
	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	126	39	16	48	28	68	609	18	25	62	264	33	0	.	68	49	498	16	120	36	205	23	103	38	18	71	59	30	133	23	49	62	110	54	81	36	235	40	68	24	73	35	289	20	287	16	33	56	386	34	3881	7
	16-23 cm	375	33	105	55	239	37	2230	12	80	75	1102	20	3	100	202	35	2076	11	364	48	831	20	319	37	137	48	270	28	459	24	130	51	110	43	364	29	576	36	235	23	330	38	918	15	1179	14	82	58	569	21	13284	5
	24-35 cm	836	29	493	34	703	41	6191	9	138	45	2329	19	0	.	782	53	6365	10	734	32	1755	18	869	26	371	44	921	25	1523	19	406	36	383	33	1204	22	585	31	735	29	987	30	2447	18	2869	13	144	46	1628	19	35398	4
	36-51 cm	2667	22	793	31	1231	28	14745	8	436	52	4773	16	0	.	1143	25	10754	8	1196	23	4522	15	1704	17	235	42	1805	26	3293	17	685	27	1720	25	2180	21	1428	26	1803	18	1115	27	4980	14	4001	11	380	49	3609	15	71198	3
	=52 cm	1965	24	590	46	575	37	11628	9	611	53	2551	19	0	.	1339	32	9042	10	932	29	3606	18	1736	24	226	52	906	29	4405	19	535	32	868	34	1899	21	1295	25	1950	22	1082	31	3697	14	3025	16	164	69	2259	20	56885	4
	total	5969	18	1997	29	2776	26	35403	7	1289	39	11018	13	3	100	3533	26	28735	7	3345	20	10919	13	4730	16	987	35	3961	22	9814	13	1805	25	3191	23	5728	16	4119	19	4791	16	3587	23	12330	11	11361	10	803	37	8452	13	180646	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	.
	12-15 cm	22	58	2	100	12	52	317	15	15	45	44	40	0	.	3	80	13	34	60	40	143	21	81	42	-14	100	32	45	42	36	5	71	38	53	14	88	63	47	11	40	54	55	74	42	17	40	28	41	-27	145	1050	10
	16-23 cm	105	41	21	61	38	59	935	14	32	42	158	33	0	.	11	62	47	35	281	31	480	23	341	28	6	72	104	45	119	27	2	100	108	29	121	33	101	44	62	45	110	57	500	24	73	38	26	86	25	110	3805	7
	24-35 cm	202	37	127	55	217	45	3012	13	105	43	676	35	0	.	7	100	188	37	778	29	1145	22	694	26	61	60	87	40	438	28	19	100	361	34	246	33	191	45	40	63	190	41	1054	18	149	49	13	100	327	33	10326	6
	36-51 cm	789	29	187	45	694	40	5877	10	130	59	883	27	0	.	205	68	512	31	1269	24	2399	16	1304	20	203	71	398	40	1409	25	51	75	471	27	593	31	217	45	412	37	159	51	1676	17	369	41	14	100	1353	22	21571	5
	=52 cm	545	30	65	71	171	41	6271	11	61	100	640	35	0	.	160	54	760	30	741	29	1513	21	1018	24	78	75	339	49	1003	25	0	.	519	39	741	30	358	54	327	58	154	68	1689	23	259	42	60	72	774	24	18245	6
	total	1663	24	402	39	1133	35	16411	9	343	51	2400	26	0	.	386	54	1519	25	3129	19	5679	15	3437	18	334	60	960	34	3012	18	77	73	1497	24	1715	25	929	34	852	33	666	37	4993	14	867	30	142	45	2453	20	54997	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	7	60	0	.	0	.	6	59	4	72	0	.	2	100	0	.	122	33	0	.	2	100	4	100	0	.	12	83	0	.	1	743	6	75	1	100	5	71	6	100	0	0	1	100	165	34	0	.	0	0	345	21
	16-23 cm	20	87	0	.	0	.	54	44	52	90	-3	100	64	90	0	.	305	24	52	100	3	100	-9	260	0	.	43	100	0	.	35	100	18	78	3	100	9	78	3	100	43	75	11	80	449	25	0	.	48	67	1201	15
	24-35 cm	72	47	0	.	0	.	300	41	72	52	34	59	18	100	0	.	667	23	104	63	79	74	32	73	0	.	42	76	0	.	45	62	43	71	36	79	0	.	40	71	114	63	53	81	572	21	0	.	232	38	2554	11
	36-51 cm	302	48	0	.	13	100	794	30	168	48	493	53	12	100	0	.	1072	22	240	42	78	62	57	80	0	.	23	100	31	100	263	45	403	29	13	100	340	35	77	74	42	100	142	47	561	29	0	.	894	20	6018	9
	=52 cm	227	37	0	.	0	.	411	40	27	100	110	51	21	100	0	.	557	29	113	57	17	100	13	100	0	.	0	.	49	74	100	71	17	100	0	.	145	47	36	71	0	.	49	100	120	41	0	.	206	43	2218	14
	total	628	35	0	.	13	100	1565	26	323	36	634	49	117	72	0	.	2723	17	509	37	179	58	97	74	0	.	120	89	80	72	444	37	487	28	53	66	499	34	162	53	199	61	257	45	1866	19	0	.	1381	20	12336	8
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	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	31	0	.	0	.	0	.	0	.	0	.	11	65	0	100	0	.	0	.	4	100	28	29	0	.	1	100	93	21	0	.	2	100	184	14
	16-23 cm	21	58	0	.	0	.	0	.	0	.	15	100	0	.	0	.	220	22	0	.	0	.	0	.	0	.	0	.	23	59	0	.	0	.	0	.	9	100	203	25	0	.	31	57	347	25	0	.	4	100	872	13
	24-35 cm	84	63	0	.	0	.	101	68	18	100	19	100	0	.	0	.	835	20	0	.	10	100	0	.	0	.	0	.	26	78	62	77	0	.	0	.	24	100	562	28	0	.	7	100	1127	18	0	.	64	62	2939	11
	36-51 cm	161	52	0	.	0	.	152	46	13	100	63	76	0	.	0	.	2432	13	19	100	22	100	0	.	0	.	0	.	47	61	0	.	0	.	0	.	10	100	821	20	0	.	105	45	2474	15	0	.	154	42	6472	8
	=52 cm	85	58	0	.	0	.	272	39	0	.	69	100	0	.	42	100	2618	15	0	.	0	.	38	100	0	.	0	.	179	58	0	.	0	.	46	100	0	.	945	27	18	100	93	82	2690	15	58	100	105	72	7257	9
	total	352	45	0	.	0	.	525	33	31	71	166	58	0	.	42	100	6147	11	19	100	32	75	38	100	0	.	0	.	287	46	63	77	0	.	46	100	48	91	2558	17	18	100	236	43	6731	12	58	100	328	47	17724	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	.	18	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	9	38	0	.	0	.	28	25
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	40	0	.	0	.	203	24
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	360	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	32	0	.	0	.	511	23
	36-51 cm	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	477	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	239	34	0	.	0	.	729	20
	=52 cm	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	354	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	36	0	.	0	.	579	22
	total	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	1316	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	718	23	0	.	0	.	2050	15
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	1	100	0	.	0	.	8	60	0	.	1	100	0	.	2	100	5	58	2	100	1	100	0	.	0	.	1	100	3	71	0	.	4	57	2	100	3	100	0	.	4	75	14	49	7	61	0	.	11	63	69	20
	16-23 cm	0	.	0	.	3	100	10	74	7	100	0	.	0	.	0	.	5	59	0	.	6	100	5	100	0	.	0	.	7	76	12	100	14	72	0	.	0	.	0	.	0	.	12	74	13	100	0	.	6	71	101	27
	24-35 cm	41	84	0	.	0	.	70	63	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	168	54
	36-51 cm	0	.	0	.	0	.	49	59	14	100	0	.	0	.	0	.	0	.	0	.	119	80	0	.	0	.	0	.	62	100	0	.	0	.	0	.	0	.	12	100	0	.	29	100	0	.	0	.	20	100	305	40
	=52 cm	0	.	0	.	0	.	149	63	0	.	0	.	0	.	0	.	0	.	0	.	59	71	0	.	0	.	0	.	96	100	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	371	42
	total	42	82	0	.	3	100	287	46	87	100	1	100	0	.	2	100	10	58	2	100	185	75	5	100	0	.	1	100	145	110	12	100	18	58	2	100	3	100	12	100	4	75	123	79	19	85	0	.	48	54	1013	28
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	147	28	4	76	3	79	496	19	78	35	62	25	0	.	21	48	40	31	102	40	78	23	28	77	31	80	30	51	132	22	91	41	77	30	46	66	46	34	289	25	10	103	144	18	34	42	1	100	44	32	2034	8
	16-23 cm	467	21	23	65	28	48	1570	11	242	32	465	28	0	.	89	33	282	30	270	31	296	21	217	25	73	50	167	38	369	28	288	29	338	22	94	48	143	44	946	20	61	42	500	19	160	38	32	63	329	21	7450	5
	24-35 cm	1060	19	64	54	134	41	3781	11	758	26	998	25	0	.	197	60	250	35	789	25	843	24	458	27	126	53	324	44	1500	22	476	30	1435	21	386	27	355	26	987	17	117	39	2017	15	243	41	77	60	913	23	18287	5
	36-51 cm	2224	18	115	50	250	39	5270	11	811	28	926	27	0	.	531	30	578	31	1674	20	1155	23	537	26	95	68	534	46	2000	20	672	28	2191	17	449	34	574	25	784	18	145	69	2576	15	141	48	108	47	1742	16	26083	4
	=52 cm	1767	23	2	1632	82	71	2899	16	954	38	482	46	0	.	63	106	391	42	943	26	547	33	73	59	22	100	406	57	785	29	221	44	715	27	355	52	365	45	503	29	169	51	952	26	55	76	0	.	1415	22	14164	7
	total	5665	15	207	50	497	34	14017	9	2844	20	2933	19	0	.	900	32	1541	23	3778	16	2919	18	1313	19	347	44	1462	37	4786	18	1747	21	4756	14	1328	27	1483	21	3508	14	503	39	6189	12	633	31	218	48	4443	14	68018	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	35	33	2	100	4	71	62	20	5	77	25	38	0	.	6	45	22	47	19	38	17	70	12	46	5	73	14	71	19	41	9	79	39	30	15	37	-20	157	19	40	3	71	73	25	17	106	2	100	67	39	470	12
	16-23 cm	87	34	0	.	21	100	314	22	48	44	89	36	0	.	67	57	27	37	55	39	87	47	92	42	22	54	76	74	234	27	9	91	69	37	52	45	33	43	21	54	23	71	144	43	62	45	17	100	152	35	1802	9
	24-35 cm	257	32	7	100	133	49	675	19	139	37	44	58	0	.	111	42	47	68	179	35	161	42	152	45	62	75	57	52	625	22	0	.	218	31	59	61	134	41	38	50	53	57	324	29	14	227	0	.	301	26	3791	8
	36-51 cm	269	32	16	72	64	62	456	21	118	39	79	46	0	.	40	82	53	52	187	33	64	48	29	74	11	100	53	59	467	22	0	.	176	28	120	39	83	78	0	.	18	65	125	34	46	70	49	66	120	38	2644	9
	=52 cm	24	100	0	.	0	.	83	42	37	100	21	100	0	.	19	100	29	71	0	.	0	.	0	.	0	.	0	.	159	44	0	.	28	100	0	.	44	72	0	.	0	.	29	71	0	.	18	100	142	46	633	20
	total	671	25	25	58	223	45	1590	14	347	28	257	28	0	.	243	38	179	35	440	22	329	26	285	38	100	50	200	53	1504	18	18	63	531	21	246	30	274	46	79	41	97	41	695	23	139	37	86	55	782	20	9341	6
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	13	53	0	.	3	100	21	39	20	67	9	60	1	100	9	63	29	50	18	52	24	33	13	47	7	59	4	71	51	38	1	100	8	62	23	75	7	47	52	38	6	58	45	41	31	40	1	100	64	89	461	16
	16-23 cm	31	62	0	.	65	71	162	25	36	53	136	37	3	100	86	72	82	58	72	59	41	69	32	74	56	64	11	72	236	25	16	71	69	42	34	74	79	43	114	31	22	54	188	42	54	35	0	.	125	45	1751	10
	24-35 cm	240	37	0	.	147	48	426	23	142	57	366	38	4	100	81	70	78	56	411	36	284	70	26	72	112	64	0	.	390	28	67	80	132	49	161	65	162	31	27	74	48	100	489	28	91	46	50	72	330	30	4263	10
	36-51 cm	465	29	0	.	27	73	378	24	151	45	170	48	0	.	145	45	28	58	358	41	381	64	43	69	93	63	16	100	238	33	37	53	176	45	187	45	351	33	-15	215	0	.	358	29	20	97	59	82	651	24	4318	10
	=52 cm	206	53	0	.	0	.	136	83	33	100	96	59	0	.	29	100	43	100	81	58	33	100	0	.	43	100	0	.	147	59	29	100	0	.	47	72	181	48	39	100	0	.	273	46	0	.	22	100	218	39	1655	17
	total	956	26	0	.	242	52	1123	21	382	40	778	27	8	100	350	52	260	45	940	36	762	58	115	59	310	53	32	81	1062	21	150	55	385	37	451	44	780	26	218	32	76	76	1352	22	195	34	132	75	1388	19	12447	7
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	16	90	0	.	0	.	-7	145	7	72	5	71	58	74	0	.	1	100	2	100	3	100	13	92	0	.	0	.	0	.	1	100	8	72	0	.	10	54	98	47	0	.	65	40	11	42	0	.	0	0	293	25
	16-23 cm	22	51	0	.	0	.	59	47	31	87	19	54	196	66	0	.	5	100	3	100	7	100	13	100	3	100	0	.	4	100	15	58	19	64	0	.	33	63	164	42	4	100	237	37	52	41	0	.	10	73	895	20
	24-35 cm	82	42	0	.	0	.	69	60	68	67	26	100	170	55	0	.	0	.	93	72	105	62	22	100	0	.	0	.	62	62	62	51	75	45	0	.	266	38	239	36	13	100	394	27	27	71	0	.	74	48	1846	13
	36-51 cm	93	48	0	.	0	.	340	25	56	90	70	51	59	85	0	.	99	71	165	45	194	54	42	74	0	.	0	.	19	75	111	66	20	71	0	.	241	31	212	47	9	100	339	38	13	100	0	.	368	27	2451	12
	=52 cm	328	38	0	.	0	.	136	48	94	74	22	100	94	100	0	.	0	.	64	58	103	71	0	.	0	.	0	.	48	100	98	80	39	100	13	100	340	53	17	100	25	100	135	65	0	.	0	.	233	43	1789	18
	total	541	30	0	.	0	.	597	22	256	52	142	46	578	47	0	.	106	71	327	49	411	45	90	62	3	100	0	.	133	50	288	46	161	42	13	100	890	30	730	25	51	66	1170	23	103	37	0	.	685	26	7274	9
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	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	350	27	0	.	1	100	0	.	0	.	0	.	380	26
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	977	17	0	.	0	.	6	100	0	.	0	.	1060	16
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1001	20	0	.	0	.	60	60	0	.	0	.	1203	17
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	579	24	0	.	0	.	-5	100	0	.	0	.	631	22
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1360	27	0	.	0	.	38	77	0	.	0	.	1425	26
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	330	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4267	14	0	.	1	100	100	57	0	.	0	.	4698	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	24	36	2	100	3	72	130	23	15	39	51	26	2	71	27	49	213	21	46	38	46	37	41	41	5	78	22	59	91	31	3	71	32	43	22	65	14	42	301	16	44	48	73	23	112	43	-1	912	31	30	1349	8
	16-23 cm	171	44	3	100	14	79	271	24	59	41	68	35	0	.	33	59	363	21	71	55	28	42	36	54	13	100	11	74	114	41	8	71	57	34	71	69	60	49	839	15	38	54	301	24	238	21	0	.	96	29	2961	7
	24-35 cm	225	34	0	.	15	78	338	29	87	48	89	60	0	.	13	72	308	31	94	36	31	71	16	100	16	100	20	100	214	31	12	100	66	47	69	41	207	40	839	21	32	59	271	43	158	30	0	.	159	43	3278	9
	36-51 cm	161	44	0	.	10	100	264	36	83	57	22	100	0	.	42	84	149	31	83	47	57	83	0	0	0	.	0	.	142	42	7	100	118	71	11	100	69	47	230	24	7	100	300	44	45	71	31	72	161	33	1991	12
	=52 cm	79	72	0	.	0	.	236	56	27	100	47	71	0	.	39	100	13	100	0	.	0	.	28	100	0	.	0	.	17	100	0	.	18	100	22	100	155	73	46	71	0	.	73	72	34	100	0	.	25	100	860	24
	total	661	28	5	73	42	79	1238	21	271	38	276	34	2	71	154	48	1046	18	294	31	162	35	120	37	34	85	53	53	578	22	29	60	292	35	194	45	505	39	2255	13	120	44	1019	24	587	21	30	77	471	27	10440	6
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	.
	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	.
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	394	20	25	52	53	47	1641	10	168	23	461	21	64	67	137	30	1032	11	368	20	519	14	295	22	51	66	175	22	482	15	161	31	323	24	203	27	367	30	1223	12	194	26	781	12	784	12	65	42	577	26	10544	4
	16-23 cm	1300	17	151	46	409	30	5605	8	587	27	2049	15	266	55	487	25	3596	8	1168	20	1778	14	1044	17	311	31	683	20	1565	14	516	25	802	16	737	22	1044	23	3564	8	631	26	2842	11	2727	10	157	37	1365	13	35386	3
	24-35 cm	3099	14	691	31	1348	30	14963	7	1593	19	4580	14	192	52	1190	38	9239	8	3183	14	4412	13	2270	15	748	30	1452	22	4757	12	1149	24	2713	16	2161	17	1925	17	4509	9	1554	22	7055	10	5460	9	283	35	4039	12	84563	2
	36-51 cm	7131	12	1111	29	2289	24	28339	6	1982	23	7479	13	71	76	2105	21	16211	7	5192	13	8989	12	3716	13	636	35	2829	21	7708	12	1825	22	5276	14	3552	16	3311	17	4915	10	1496	23	10632	9	7903	9	641	37	9072	10	144411	2
	=52 cm	5225	15	657	45	828	30	22220	7	1845	29	4036	17	115	84	1690	27	13831	8	2873	16	5878	14	2906	18	369	42	1651	25	6889	15	983	25	2205	22	3122	19	2883	19	5222	13	1448	28	7057	12	6447	11	322	46	5378	13	106081	3
	total	17149	10	2635	28	4929	22	72768	5	6174	17	18604	11	709	45	5610	20	43910	5	12783	11	21576	10	10231	12	2115	28	6789	18	21401	10	4633	18	11320	12	9776	14	9530	14	19433	6	5323	19	28366	8	23321	7	1468	31	20430	9	380985	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. 1360345. Birmensdorf, Swiss Federal Research Institute WSL

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