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
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 2009/17
		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	136	18	22	36	33	40	61	28	53	28	129	23	219	22	121	25	126	22	47	39	66	28	225	19	182	15	51	33	1471	7
	21-30 cm	158	25	71	42	90	58	109	33	54	44	280	25	390	33	212	40	331	26	139	30	107	36	323	22	436	19	96	31	2795	9
	31-40 cm	163	33	22	71	42	60	96	51	120	37	249	29	385	22	293	28	482	26	97	43	103	44	476	20	487	24	72	39	3088	8
	41-50 cm	192	35	50	58	189	42	63	50	178	33	344	26	478	24	166	31	502	22	31	73	91	43	347	21	345	20	222	38	3197	8
	51-60 cm	140	59	22	100	111	55	60	71	99	50	426	40	187	37	182	36	384	26	144	41	144	45	254	29	545	24	83	52	2780	11
	61-70 cm	0	0	0	0	0	0	71	71	56	71	133	60	238	36	139	45	84	58	24	100	70	74	23	100	197	36	44	71	1079	17
	71-80 cm	0	0	0	0	0	0	90	71	0	0	258	77	42	100	0	0	67	71	40	100	0	0	0	0	0	0	84	58	580	39
	81-90 cm	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	34	100	0	0	81	58	115	50
	91-100 cm	0	0	0	.	0	0	0	.	0	.	0	0	0	0	45	100	0	.	0	0	48	100	40	100	0	0	0	0	133	58
	>100 cm	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	48	100	0	0	0	0	0	0	48	100
	total	788	23	187	30	465	32	549	25	561	21	1819	26	1939	17	1157	19	1975	18	522	25	677	23	1722	14	2192	14	734	23	15286	6
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	97	19	2	73	16	48	29	41	16	51	18	48	71	30	33	36	15	46	22	43	8	82	9	48	3	73	4	75	342	11
	21-30 cm	153	32	0	0	0	0	74	39	43	58	5	100	113	32	65	51	5	100	15	71	13	72	19	72	9	100	22	71	535	16
	31-40 cm	165	35	20	100	0	0	70	52	40	71	55	75	69	41	70	53	89	47	39	59	15	100	0	0	43	61	0	0	674	17
	41-50 cm	133	39	31	71	0	0	46	74	98	57	44	72	252	31	54	71	13	100	52	59	0	0	0	0	42	58	48	58	811	17
	51-60 cm	46	71	0	0	23	100	40	71	67	58	34	100	193	38	62	59	0	0	18	100	25	100	72	75	0	0	0	0	580	22
	61-70 cm	100	72	0	0	0	0	0	0	0	0	100	73	156	50	0	0	0	0	0	0	0	0	0	0	33	100	32	100	421	32
	71-80 cm	0	0	0	0	0	0	0	0	39	100	0	0	0	0	54	100	0	0	0	0	0	0	39	100	0	0	0	0	132	58
	81-90 cm	0	0	0	.	0	0	46	100	0	0	0	.	0	0	0	0	55	100	0	.	0	0	0	0	0	0	0	0	101	71
	91-100 cm	53	100	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	748	21	53	57	39	61	306	29	303	33	256	47	853	21	337	32	177	51	146	33	60	51	139	49	129	45	106	42	3651	9
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	18	42	0	0	3	71	0	0	9	71	0	.	7	100	0	.	13	61	4	100	6	100	62	29	46	34	2	100	168	17
	21-30 cm	52	60	35	66	30	58	0	0	10	100	0	.	0	0	0	.	17	71	0	0	17	80	97	31	58	38	19	74	335	18
	31-40 cm	68	42	36	71	59	62	39	100	30	58	0	.	0	0	0	0	5	100	18	81	0	0	115	33	24	59	25	71	419	20
	41-50 cm	57	51	28	100	19	100	20	100	17	100	0	.	0	0	0	0	28	73	0	0	9	100	45	78	22	73	0	0	245	28
	51-60 cm	0	0	0	0	0	0	27	100	0	0	0	.	14	100	0	0	0	0	0	0	0	.	13	100	18	100	14	100	87	47
	61-70 cm	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	20	100	0	0	20	100
	71-80 cm	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
	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	194	33	99	56	111	51	86	71	65	41	0	.	21	75	0	0	64	55	22	70	31	72	332	21	189	30	59	49	1274	13
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	4	100	0	.	2	100	0	0	0	0	0	0	1	100	0	0	9	71	0	.	6	71	99	26	24	38	35	26	180	17
	21-30 cm	8	100	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	.	0	0	152	26	93	38	43	39	296	19
	31-40 cm	0	0	0	0	11	100	0	0	0	0	0	.	0	.	0	0	30	100	0	.	0	0	161	29	57	47	33	51	291	22
	41-50 cm	0	.	0	0	18	100	0	0	0	0	0	.	0	0	0	.	0	0	0	.	11	100	138	34	115	38	54	55	336	22
	51-60 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	.	0	0	138	39	51	58	16	100	205	31
	61-70 cm	0	.	0	.	28	100	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	63	73	45	71	47	71	183	39
	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
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	33	100	82	71	115	58
	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	0
	total	12	100	0	0	59	61	0	0	0	0	0	0	1	100	0	0	38	79	0	.	16	85	752	19	417	26	312	27	1607	13
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	.	3	100	16	44	0	0	19	40
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	9	73	0	0	15	59
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	7	100	0	.	22	58
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	7	100	9	100	0	.	16	71
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	14	100	0	.	25	71
	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	.	0	0	0	0	0	.	0	0
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	71	0	.	0	.	62	71
	>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	.	104	53	55	48	0	0	159	38
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	4	59	0	0	2	100	0	0	0	0	0	.	0	0	0	0	3	71	0	0	0	0	1	100	8	52	0	.	18	32
	21-30 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	5	100	8	100	0	0	0	.	14	72
	31-40 cm	0	.	0	0	0	0	0	0	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	.	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
	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
	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	4	59	0	0	2	100	0	0	0	0	0	.	0	0	0	0	3	71	0	0	5	100	9	88	8	52	0	0	31	36
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	54	25	36	43	3	58	14	42	18	32	1	100	39	30	31	27	10	41	6	63	7	49	5	59	9	45	30	46	265	12
	21-30 cm	118	31	32	58	0	0	12	74	59	35	0	0	46	40	33	47	24	57	0	0	25	47	7	71	6	73	57	46	419	15
	31-40 cm	124	53	49	45	18	100	27	78	16	71	16	100	85	61	46	53	4	100	0	0	5	100	0	0	6	100	38	63	434	23
	41-50 cm	27	72	50	58	38	58	92	36	11	100	0	0	110	58	27	72	14	71	0	0	17	73	0	0	8	100	29	58	423	21
	51-60 cm	24	100	90	62	34	71	0	0	60	58	0	0	34	72	41	72	0	0	0	0	0	0	0	0	0	0	49	60	332	27
	61-70 cm	23	100	0	0	0	0	50	72	0	0	0	0	0	0	30	100	0	0	0	0	0	0	0	0	0	0	0	0	102	51
	71-80 cm	0	0	0	0	28	100	0	0	42	100	0	0	43	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	113	59
	81-90 cm	0	.	45	100	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	45	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	369	26	303	38	121	39	195	31	206	30	17	95	357	37	208	30	53	40	6	63	54	35	12	54	29	60	204	32	2134	11
maple	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12	48	5	73	2	100	8	100	16	48	3	100	8	60	6	100	11	64	3	100	7	75	11	52	2	100	8	50	100	19
	21-30 cm	17	72	0	0	0	0	11	71	24	58	0	0	0	0	14	100	5	100	0	0	10	100	7	100	0	0	0	0	88	32
	31-40 cm	0	0	0	0	6	100	0	0	6	100	10	100	0	0	16	100	0	0	0	0	8	100	0	0	0	0	0	0	47	48
	41-50 cm	0	0	12	100	0	0	0	0	0	0	0	0	0	0	9	100	0	0	0	0	9	100	12	100	0	0	0	0	42	50
	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
	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	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	46	17	75	9	77	19	58	46	50	13	83	8	60	45	53	16	54	3	100	34	49	29	60	2	100	8	50	278	18
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12	64	7	71	1	100	2	100	14	50	0	0	14	42	7	71	3	71	2	100	0	0	9	55	13	61	27	51	111	20
	21-30 cm	30	50	8	100	9	100	5	100	20	73	5	100	9	100	21	71	8	100	0	0	8	100	21	75	0	0	22	61	166	23
	31-40 cm	0	0	0	0	0	0	10	100	50	81	0	0	17	100	15	100	15	100	0	0	7	100	0	0	0	0	7	100	121	42
	41-50 cm	0	0	0	0	0	0	17	100	39	71	0	0	24	72	0	0	0	0	0	0	0	0	0	0	0	0	0	0	80	46
	51-60 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	14	100	0	0	0	0	0	.	0	0	14	100
	61-70 cm	0	0	0	0	0	0	0	0	32	100	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	32	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
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	42	40	14	63	11	88	34	59	155	48	5	100	64	46	43	50	26	65	16	100	14	100	30	62	13	61	56	43	523	19
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	22	41	6	73	7	50	4	100	0	0	3	100	0	0	0	0	3	72	0	0	0	0	6	60	0	0	30	41	81	22
	21-30 cm	0	0	15	100	43	53	0	0	0	0	0	0	0	0	20	100	0	0	0	0	0	.	0	0	0	0	14	74	92	38
	31-40 cm	27	100	0	0	8	100	0	0	11	100	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	47	65
	41-50 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
	51-60 cm	0	0	0	0	26	100	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	26	100
	61-70 cm	0	0	0	0	0	0	26	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	26	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	.	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	49	58	21	74	84	52	30	88	11	100	3	100	0	0	20	100	3	72	0	0	0	0	6	60	0	0	44	42	272	25
sweet chestnut	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	75	0	.	357	17	376	17
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	143	28	143	28
	31-40 cm	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	93	34	93	34
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	0	84	45	84	45
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	22	75	22	75
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	28	71	42	58
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	33	70	33	70
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	31	100	31	100
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	25	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	total	0	.	0	.	2	100	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	32	84	0	0	814	16	848	15
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	54	25	12	47	30	52	2	100	27	32	53	38	56	36	33	35	73	31	25	50	17	56	73	25	79	25	175	15	709	8
	21-30 cm	28	59	0	0	28	52	0	0	35	46	4	100	20	74	32	54	13	73	14	58	23	52	45	36	60	39	265	24	567	14
	31-40 cm	14	71	12	100	0	0	0	0	20	71	13	100	0	0	7	100	20	100	6	100	12	100	21	73	28	100	119	39	272	24
	41-50 cm	16	100	12	100	11	100	0	0	20	73	0	.	0	0	0	0	8	100	0	0	11	100	9	100	9	100	40	50	134	28
	51-60 cm	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	18	100	0	0	0	0	0	0	0	0	0	0	18	100
	61-70 cm	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	31	100	0	0	0	.	0	0	0	0	31	100
	71-80 cm	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
	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	113	27	36	50	69	40	2	100	101	31	70	35	76	33	72	36	132	29	75	46	63	39	148	23	176	30	598	16	1731	8
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12	58	4	100	1	100	4	100	3	72	8	53	5	65	5	55	13	47	2	100	0	.	9	43	17	43	19	50	100	18
	21-30 cm	9	100	0	.	0	.	0	.	0	.	10	100	27	52	6	100	8	100	0	.	9	100	10	74	12	73	7	100	98	28
	31-40 cm	32	100	0	.	0	.	0	.	20	100	7	100	0	.	10	100	40	59	0	.	0	.	0	.	15	71	22	75	145	34
	41-50 cm	10	100	13	100	0	.	0	.	0	.	0	.	55	50	0	.	54	60	36	73	0	.	9	100	36	58	0	.	212	27
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	14	100	34	71	0	.	0	.	62	50
	61-70 cm	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	36	71
	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	63	56	17	80	1	100	4	100	23	89	41	51	88	40	20	56	129	37	38	69	23	72	80	41	79	41	48	50	654	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	425	10	94	24	103	23	125	20	154	16	214	21	420	14	236	16	278	15	111	23	116	22	529	12	400	11	736	10	3939	4
	21-30 cm	572	15	161	32	200	34	211	24	245	21	303	23	605	23	401	24	411	22	168	26	218	25	695	15	683	15	688	13	5563	5
	31-40 cm	594	19	139	31	145	34	242	31	314	25	350	25	556	20	457	23	685	20	160	31	150	35	788	15	666	20	409	17	5655	6
	41-50 cm	434	21	196	31	275	32	237	28	362	25	388	27	919	18	255	26	620	19	119	44	147	31	567	17	584	17	477	24	5581	6
	51-60 cm	209	44	112	53	194	38	127	46	227	32	460	39	428	26	285	33	417	25	176	36	182	40	522	24	627	21	185	32	4151	9
	61-70 cm	123	62	0	0	28	100	146	46	88	58	250	44	394	29	169	41	84	58	55	71	70	74	120	47	295	29	152	39	1974	13
	71-80 cm	0	0	0	0	28	100	90	71	81	71	258	77	85	71	54	100	67	71	40	100	0	0	39	100	0	0	117	46	858	29
	81-90 cm	0	0	45	100	0	0	46	100	0	0	0	0	0	0	0	0	55	100	0	0	0	0	34	100	33	100	194	41	407	31
	91-100 cm	53	100	0	.	0	0	0	0	0	0	0	0	0	0	45	100	0	.	0	0	48	100	102	58	0	0	25	100	273	39
	>100 cm	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	.	48	100	0	0	0	0	0	0	48	100
	total	2411	12	748	22	972	21	1224	16	1471	14	2224	22	3406	13	1902	14	2616	15	829	20	979	19	3396	10	3288	11	2983	9	28448	4

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. 1306716. Birmensdorf, Swiss Federal Research Institute WSL

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