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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NFI5
volume of deadwood (stemwood)
main tree species · diameter at breast height (10 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
main tree species	diameter at breast height (10 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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	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	.
spruce	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	163	18	20	43	14	40	51	30	53	30	122	23	203	21	191	24	164	20	41	36	52	29	251	19	245	14	54	26	1624	6
	21-30 cm	187	24	62	39	74	44	88	43	116	33	287	30	418	27	319	30	246	30	130	31	103	37	272	22	495	17	192	28	2991	8
	31-40 cm	431	31	33	58	104	52	93	42	98	50	212	30	595	19	307	26	553	21	105	43	145	37	665	18	638	21	215	29	4195	8
	41-50 cm	380	29	152	75	107	49	47	58	134	36	373	22	506	23	287	31	425	20	39	58	151	35	476	17	665	20	224	27	3966	8
	51-60 cm	310	34	45	100	43	72	112	51	259	40	244	43	337	31	432	30	377	28	137	38	105	56	478	26	746	21	312	40	3940	9
	61-70 cm	57	71	39	100	0	0	0	0	102	74	88	61	349	36	31	100	100	51	101	50	108	50	163	38	182	38	183	55	1503	16
	71-80 cm	45	100	0	0	0	0	52	100	41	100	170	58	149	59	47	100	61	71	0	0	28	100	90	58	65	71	106	62	852	23
	81-90 cm	0	0	0	.	0	0	92	71	0	0	0	0	0	0	44	100	0	0	0	0	0	0	0	0	0	0	54	71	189	46
	91-100 cm	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	49	100	0	0	0	0	0	0	49	100
	>100 cm	106	100	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	106	100
	total	1678	19	351	52	343	37	535	25	804	25	1497	21	2557	14	1658	17	1927	16	553	25	741	21	2397	13	3037	13	1340	20	19416	5
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	83	21	4	59	7	58	20	48	24	43	21	41	49	33	40	37	11	51	14	44	6	62	8	65	4	100	4	67	296	12
	21-30 cm	138	35	24	60	0	0	65	39	26	71	5	100	133	31	85	43	15	59	40	46	7	100	24	71	31	65	15	73	606	15
	31-40 cm	236	24	30	74	0	0	99	43	44	61	112	57	109	32	68	58	69	42	30	100	24	72	25	73	42	53	30	58	917	14
	41-50 cm	133	40	28	71	0	0	72	53	119	45	49	58	238	43	137	42	28	71	70	51	0	0	17	100	15	100	75	45	980	16
	51-60 cm	114	45	0	0	45	71	46	71	44	71	63	71	127	46	25	100	39	100	17	100	0	0	43	71	52	72	24	100	638	20
	61-70 cm	166	45	131	50	97	74	30	100	0	0	43	100	317	40	0	0	0	0	37	100	0	0	38	100	40	100	30	100	928	21
	71-80 cm	81	71	0	0	0	0	120	74	34	100	0	0	103	71	135	58	0	0	0	0	0	0	0	0	0	0	0	0	474	33
	81-90 cm	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	55	100	0	.	0	0	0	.	0	0	0	0	55	100
	91-100 cm	53	100	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	53	100
	>100 cm	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	1004	18	217	35	148	65	451	29	291	29	293	38	1075	21	490	29	218	41	208	34	37	51	156	41	183	47	178	38	4947	9
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	20	40	4	100	0	0	3	100	8	71	0	.	18	76	0	.	11	51	4	100	2	100	104	23	73	30	2	100	248	15
	21-30 cm	78	48	24	72	16	100	0	0	17	71	0	.	0	0	0	.	13	72	5	100	12	72	112	28	92	39	19	73	388	18
	31-40 cm	112	34	36	50	0	0	27	100	26	73	0	.	8	100	0	0	13	74	19	81	0	0	127	31	72	41	39	57	480	16
	41-50 cm	40	59	32	75	14	100	13	100	54	50	0	.	19	100	0	0	13	100	0	0	10	100	51	53	34	58	0	0	280	23
	51-60 cm	0	0	0	0	0	0	40	71	21	100	0	.	14	100	0	0	0	0	0	0	0	0	21	100	46	58	11	100	153	34
	61-70 cm	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	29	100	0	0	29	100
	71-80 cm	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	81-90 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	250	26	97	49	30	71	83	57	126	43	0	.	60	55	0	0	50	51	28	59	23	73	414	22	345	26	70	51	1577	11
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	0	1	100	0	0	0	0	4	100	2	100	5	100	8	71	0	.	5	71	104	23	58	31	39	25	227	15
	21-30 cm	0	.	0	0	0	0	0	0	7	100	0	.	0	0	0	0	0	0	0	.	0	0	198	27	124	34	69	29	398	18
	31-40 cm	0	.	0	0	20	72	0	.	0	0	0	0	0	.	0	.	29	100	0	.	0	0	153	30	108	33	69	47	379	19
	41-50 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	13	100	0	.	9	100	177	30	98	42	103	48	399	21
	51-60 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	.	0	0	90	46	83	45	95	46	267	26
	61-70 cm	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	71	58	73	58	37	75	181	36
	71-80 cm	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	35	100	80	71	115	58
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0
	total	0	0	0	0	21	67	0	0	7	100	4	100	2	100	5	100	50	64	0	.	13	86	793	17	578	22	492	26	1966	12
Arolla pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	52	13	51	0	0	20	38
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	52	5	100	0	0	31	46
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	22	58	6	100	0	.	28	50
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	9	100	0	.	16	71
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	28	71	0	.	39	58
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	0	0	.	29	100
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	26	100
	>100 cm	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	.	127	41	61	47	0	0	188	32
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3	74	0	0	0	.	4	100	0	0	0	.	0	0	0	0	2	71	3	100	0	0	1	100	13	49	0	0	26	32
	21-30 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	5	100	0	0	0	0	0	.	5	100
	31-40 cm	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	11	100	0	.	0	.	11	100
	41-50 cm	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	51-60 cm	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	61-70 cm	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	71-80 cm	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	81-90 cm	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3	74	0	0	0	0	4	100	0	0	0	.	0	0	0	0	2	71	3	100	5	100	12	92	13	49	0	0	41	35
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	122	23	45	34	26	36	18	44	14	47	12	63	46	32	26	32	15	32	6	52	38	43	16	48	5	46	62	28	451	10
	21-30 cm	225	29	40	45	22	65	7	100	49	38	5	100	66	45	70	34	3	100	0	0	33	47	18	73	19	70	53	39	611	14
	31-40 cm	165	38	94	41	18	72	50	48	6	100	50	52	62	51	119	37	9	100	0	0	46	57	11	100	10	71	57	43	697	15
	41-50 cm	84	38	49	58	31	58	46	45	75	46	0	0	124	34	89	38	6	100	6	100	13	71	0	0	8	100	43	51	575	15
	51-60 cm	66	58	210	41	0	0	20	100	34	72	0	0	0	0	52	74	14	100	0	0	14	100	0	0	13	100	30	72	454	24
	61-70 cm	23	100	110	78	22	100	0	0	0	0	0	0	57	100	28	100	0	0	0	0	0	0	0	0	0	0	0	0	240	47
	71-80 cm	0	0	40	100	0	0	31	100	26	100	0	0	33	100	0	0	0	0	0	0	0	0	0	0	20	100	0	0	150	46
	81-90 cm	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	37	100	0	.	0	0	37	100
	91-100 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	685	18	588	27	119	41	173	37	205	29	67	42	388	27	383	25	48	41	12	58	144	30	82	53	75	55	246	27	3215	9
maple	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	29	42	15	50	0	0	4	100	15	58	2	100	9	61	11	71	17	64	5	63	5	77	19	51	0	0	9	56	140	18
	21-30 cm	24	59	7	100	0	0	12	100	23	61	0	0	13	100	0	0	10	100	0	0	33	53	0	0	0	0	12	100	135	27
	31-40 cm	14	71	0	0	12	100	0	0	0	0	0	0	9	100	16	100	16	100	0	0	0	0	0	0	0	0	0	0	67	43
	41-50 cm	0	0	12	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	9	100	0	0	0	0	0	0	21	71
	51-60 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
	61-70 cm	0	0	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0
	71-80 cm	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68	37	33	46	12	100	16	80	39	57	2	100	31	54	27	66	42	50	5	63	47	45	19	51	0	0	21	78	363	16
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	57	35	42	43	10	57	10	53	30	40	10	65	52	35	28	40	9	58	6	71	7	69	16	65	8	63	23	54	310	14
	21-30 cm	27	63	26	78	12	71	0	0	63	48	27	74	65	47	75	47	30	83	21	58	22	66	19	71	17	100	24	64	427	18
	31-40 cm	139	74	10	100	0	0	22	100	34	58	0	0	67	56	60	46	19	100	0	0	24	77	0	0	0	0	16	100	390	31
	41-50 cm	53	58	12	100	0	0	16	100	129	39	0	0	27	70	19	100	10	100	0	0	32	72	8	100	11	100	29	71	346	22
	51-60 cm	0	0	0	0	28	100	0	0	42	71	0	.	0	0	0	0	0	0	0	0	18	100	0	0	0	.	0	0	88	51
	61-70 cm	0	0	0	0	0	0	0	0	33	100	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	33	100
	71-80 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
	81-90 cm	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	276	45	90	35	51	59	48	64	331	30	37	68	210	33	182	31	69	66	27	54	103	52	43	43	36	67	92	45	1594	13
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	24	42	7	71	16	51	10	58	2	100	2	100	1	100	0	.	1	100	0	0	0	0	6	49	0	0	34	40	103	20
	21-30 cm	32	48	13	100	25	61	0	0	0	0	0	0	0	0	22	100	0	0	9	100	0	.	0	0	0	0	29	51	131	29
	31-40 cm	14	71	0	0	22	73	0	0	11	100	0	0	0	.	0	0	0	0	0	0	0	.	5	100	0	0	10	100	62	40
	41-50 cm	8	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	24	73	33	60
	51-60 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
	61-70 cm	0	0	0	0	0	0	0	0	0	0	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	0	21	100	42	71
	71-80 cm	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0
	81-90 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	>100 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	78	31	20	71	63	43	10	58	13	84	2	100	22	97	22	100	1	100	9	100	0	0	11	63	0	0	118	34	370	18
sweet chestnut	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	79	0	0	504	17	511	16
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	230	24	230	24
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	137	35	137	35
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	9	100	0	0	100	32	109	30
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	52	46	52	46
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	41	58	57	50
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	84	53	84	53
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	19	100
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	23	100	23	100
	>100 cm	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	.	31	85	0	0	1191	14	1223	13
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	55	26	16	40	31	35	11	53	27	30	56	41	31	33	51	47	63	34	38	53	34	40	109	22	87	28	239	15	847	8
	21-30 cm	47	55	38	67	13	74	26	60	31	45	13	76	31	59	65	48	46	42	18	59	25	64	42	34	72	33	300	24	767	13
	31-40 cm	71	62	8	100	15	100	0	0	7	100	0	0	0	0	18	100	34	60	0	0	16	71	20	58	23	60	173	34	386	22
	41-50 cm	0	0	10	100	24	71	0	0	11	100	0	0	0	0	0	.	22	100	0	0	21	71	17	72	17	74	50	45	173	26
	51-60 cm	18	100	0	0	39	100	0	0	0	0	0	.	0	0	0	0	39	71	0	0	16	100	0	0	0	0	0	0	112	48
	61-70 cm	0	.	0	0	0	0	0	0	0	0	0	.	19	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	19	100
	71-80 cm	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0
	81-90 cm	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	91-100 cm	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	>100 cm	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	191	30	73	42	122	53	37	55	76	32	69	36	80	36	133	35	205	32	55	47	112	39	188	20	199	23	763	17	2303	9
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	14	48	0	.	0	.	2	100	3	70	5	67	6	50	11	51	8	57	0	.	3	100	8	46	14	39	12	44	84	17
	21-30 cm	32	59	0	.	0	.	0	.	7	100	7	100	23	51	0	.	7	100	0	.	0	.	19	76	30	66	9	100	134	27
	31-40 cm	29	71	0	.	0	.	0	.	21	100	7	100	27	59	18	71	55	51	0	.	6	100	6	100	35	61	16	100	221	24
	41-50 cm	9	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	69	51	22	100	9	100	9	100	33	60	0	.	163	31
	51-60 cm	0	.	0	.	0	.	0	.	0	.	17	100	35	72	0	.	0	.	0	.	0	.	35	71	0	.	0	.	87	45
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	19	100
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	84	42	13	100	0	.	2	100	30	73	36	55	91	35	29	48	139	36	22	100	18	87	95	39	113	33	36	59	708	14
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	569	10	152	20	106	20	133	20	177	16	234	19	416	14	364	16	311	14	117	24	151	20	656	11	519	11	981	10	4887	4
	21-30 cm	791	14	234	24	162	27	198	25	340	20	344	26	747	18	635	19	371	22	223	23	240	22	729	14	886	14	953	12	6853	5
	31-40 cm	1211	18	212	25	191	34	292	24	248	27	381	27	878	15	605	20	797	17	153	37	261	28	1045	13	934	16	762	15	7969	5
	41-50 cm	707	19	308	41	176	34	194	30	521	20	422	22	913	18	533	21	586	18	137	39	254	27	771	15	889	17	649	16	7060	6
	51-60 cm	508	24	255	38	155	42	218	34	400	29	324	36	514	24	509	28	470	25	154	36	153	42	677	21	968	17	523	27	5830	7
	61-70 cm	246	35	280	41	118	63	30	100	135	61	131	52	763	28	59	71	100	51	139	45	108	50	307	28	323	29	312	36	3051	11
	71-80 cm	126	58	40	100	0	0	203	53	101	58	170	58	285	42	182	50	61	71	0	0	28	100	90	58	85	59	190	41	1560	17
	81-90 cm	0	0	0	0	0	0	92	71	0	0	0	0	0	0	44	100	55	100	0	0	0	0	66	71	35	100	153	46	444	30
	91-100 cm	53	100	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	49	100	26	100	0	0	23	100	152	53
	>100 cm	106	100	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	106	100
	total	4317	10	1481	19	909	21	1359	16	1922	14	2006	18	4516	11	2930	13	2751	13	922	19	1244	16	4368	9	4639	10	4547	8	37911	3

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. 1306743. Birmensdorf, Swiss Federal Research Institute WSL

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