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
growing stock (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	766	17	208	30	291	48	823	21	714	18	539	22	1096	17	415	21	635	15	291	23	293	25	1040	14	2014	11	610	16	9734	5
	21-30 cm	1480	13	253	30	594	29	1206	20	1655	17	1182	21	2033	12	1599	15	1200	16	733	21	590	31	2316	15	4071	10	1339	15	20250	4
	31-40 cm	2779	12	359	38	606	27	1236	20	2087	15	2128	17	3095	12	3194	13	2236	14	1056	22	740	28	3033	12	5572	9	2221	18	30344	4
	41-50 cm	3407	10	433	30	816	26	1576	20	2438	13	2610	15	4023	11	3270	13	3285	14	1393	25	1059	20	3260	12	6958	9	2691	16	37221	3
	51-60 cm	3284	11	473	32	843	30	1171	19	1731	17	2136	16	3759	12	4107	12	2670	15	1126	21	1234	21	2729	13	5653	10	2453	18	33369	4
	61-70 cm	2465	15	444	30	443	34	1022	25	1230	19	1630	21	2243	17	3043	16	1764	17	886	24	737	29	1502	19	3266	13	1425	17	22098	5
	71-80 cm	1028	23	327	47	229	47	487	33	712	32	513	32	1315	19	1352	25	1148	22	677	34	817	31	1011	28	1622	18	1016	29	12255	7
	81-90 cm	345	48	0	.	155	71	151	58	454	39	257	52	389	39	488	35	277	42	135	62	185	45	278	43	913	27	440	39	4469	12
	91-100 cm	127	71	0	.	0	.	54	100	115	71	180	58	48	100	0	.	296	45	0	.	210	51	124	61	317	41	215	58	1685	19
	>100 cm	81	100	0	.	71	100	84	100	0	.	36	100	130	71	179	58	49	100	0	.	90	71	64	100	306	50	184	72	1274	24
	total	15763	7	2497	19	4049	19	7809	12	11136	10	11210	12	18130	8	17646	10	13561	11	6297	17	5955	17	15359	9	30693	7	12596	12	172699	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	862	12	39	32	134	30	477	20	187	33	69	102	648	17	222	25	86	36	126	42	23	44	90	36	18	39	66	35	3046	7
	21-30 cm	1869	13	162	37	156	44	922	23	418	28	323	44	1230	15	541	31	235	31	218	32	35	44	165	36	32	50	132	35	6435	7
	31-40 cm	2291	13	222	41	320	37	1103	19	403	36	592	28	1607	17	774	22	248	32	316	37	124	52	312	34	133	36	372	34	8817	7
	41-50 cm	3235	11	288	36	354	34	1272	17	1071	20	491	27	1782	14	1175	17	420	33	546	35	257	33	340	34	185	35	300	32	11716	5
	51-60 cm	3201	12	139	45	486	34	1824	18	1054	21	945	23	2644	14	1795	21	552	27	348	38	244	47	452	39	320	33	612	28	14617	6
	61-70 cm	1861	19	101	58	339	35	1281	22	957	25	547	44	1831	18	987	23	604	33	138	52	39	100	99	75	290	40	138	50	9211	8
	71-80 cm	1143	20	53	100	246	48	565	30	758	27	210	51	1046	24	681	27	546	35	367	61	95	71	184	61	267	46	195	50	6357	10
	81-90 cm	392	42	0	.	251	53	372	43	422	43	63	100	606	37	323	51	63	100	0	.	132	70	0	.	152	72	50	100	2828	16
	91-100 cm	214	58	0	.	0	.	68	100	58	100	0	.	61	100	58	100	0	.	68	100	78	100	0	.	0	.	204	58	807	29
	>100 cm	174	72	0	.	0	.	0	.	0	.	0	.	133	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	307	60
	total	15241	8	1003	29	2287	22	7884	12	5327	14	3240	21	11587	10	6558	13	2754	21	2127	28	1025	32	1642	26	1397	24	2068	24	64140	4
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	12	68	-22	128	2	100	14	61	12	100	0	.	28	75	0	.	8	78	16	71	-18	100	220	33	247	26	14	65	534	20
	21-30 cm	194	47	105	60	14	100	44	55	54	46	0	.	99	73	0	.	4	100	68	63	16	71	641	23	455	26	5	100	1699	14
	31-40 cm	451	32	208	31	108	52	82	69	168	31	0	.	152	53	8	100	35	83	132	50	31	100	547	22	528	27	42	106	2491	12
	41-50 cm	361	26	174	38	110	46	165	37	480	26	0	.	242	63	8	100	24	59	63	53	17	100	347	26	627	26	63	57	2681	11
	51-60 cm	289	29	87	50	161	63	164	40	416	30	0	.	119	55	53	100	9	100	55	77	12	100	145	42	522	27	27	100	2060	13
	61-70 cm	47	71	0	.	62	71	114	50	102	75	0	.	81	71	41	100	0	.	0	.	0	.	17	100	219	35	14	100	698	22
	71-80 cm	41	100	0	.	0	.	89	71	72	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	71	0	.	264	38
	81-90 cm	0	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	100	0	.	57	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1394	22	552	27	457	43	672	28	1352	19	0	.	721	45	111	61	80	48	334	45	58	60	1918	18	2717	17	165	49	10531	8
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	7	88	63	89	5	100	54	54	15	73	10	89	4	100	20	56	0	.	12	68	393	24	147	21	263	24	993	13
	21-30 cm	0	.	14	100	83	72	9	100	73	48	0	.	9	100	34	100	68	64	0	.	31	87	771	17	429	27	949	18	2469	11
	31-40 cm	0	.	5	201	54	84	0	.	61	57	10	100	0	.	0	.	72	57	0	.	81	77	1290	18	947	16	1320	17	3839	10
	41-50 cm	12	100	29	71	71	61	39	74	106	55	0	.	0	.	13	100	70	84	0	.	29	100	1924	17	1612	15	1237	14	5143	9
	51-60 cm	22	100	28	100	22	100	44	71	223	37	0	.	0	.	57	100	104	53	0	.	44	100	2081	15	1696	16	1233	15	5553	8
	61-70 cm	0	.	0	.	34	100	40	100	94	58	0	.	0	.	0	.	23	100	0	.	27	100	1542	19	1343	20	738	21	3841	11
	71-80 cm	41	100	0	.	0	.	0	.	0	.	0	.	0	.	84	71	62	71	0	.	50	100	789	26	564	26	434	28	2026	15
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	451	39	303	44	447	39	1202	23
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	228	45	121	72	38	100	387	36
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	71	129	100	236	63
	total	75	64	81	47	326	39	137	50	611	26	25	81	19	94	192	48	419	36	0	.	274	52	9470	12	7270	11	6789	11	25688	6
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	.	117	57	144	28	3	85	263	30
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	187	29	384	23	5	100	576	18
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	204	31	438	23	0	.	654	18
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	33	320	24	0	.	476	19
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	54	246	27	0	.	338	25
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	71	67	59	0	.	107	45
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	50	65	58	0	.	151	38
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	75	70	71	0	.	138	51
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	71	0	.	0	.	57	71
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	36	100	0	.	75	71
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	1047	22	1768	18	9	94	2836	13
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	46	42	11	63	0	.	20	55	55	46	0	.	3	61	20	47	11	87	12	71	8	69	9	56	34	34	1	100	233	17
	21-30 cm	24	60	35	61	6	100	5	100	66	39	0	.	8	100	16	71	30	74	0	.	0	0	4	100	8	73	0	.	202	23
	31-40 cm	0	.	18	71	7	100	63	49	50	84	0	.	0	.	0	.	5	100	0	.	-25	100	0	0	0	.	0	.	119	50
	41-50 cm	0	.	57	58	50	100	48	52	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	191	36
	51-60 cm	0	.	142	68	0	.	166	66	100	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	436	39
	61-70 cm	0	.	75	100	0	.	238	57	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	377	43
	71-80 cm	0	.	0	.	0	.	129	58	148	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	277	60
	81-90 cm	0	.	61	100	0	.	122	71	113	100	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	376	46
	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	71	34	399	66	62	91	791	41	580	61	0	.	92	88	36	41	46	77	12	71	-16	122	13	58	42	35	82	98	2211	25
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	1021	11	405	18	248	29	427	17	534	15	112	38	502	19	309	24	204	39	80	33	197	39	113	32	58	31	934	14	5144	5
	21-30 cm	2243	11	642	20	491	26	670	18	1037	16	371	28	1318	16	773	19	367	30	331	30	233	37	274	38	198	34	1733	17	10682	5
	31-40 cm	3861	10	1151	18	825	22	1177	17	1576	15	411	30	1446	15	1570	16	540	24	399	32	284	33	247	34	234	29	1459	15	15180	5
	41-50 cm	3968	9	1667	17	1089	21	1502	14	1695	14	374	36	1823	19	2082	16	702	29	252	32	646	26	89	70	360	36	975	16	17225	5
	51-60 cm	2706	12	1272	23	893	25	1883	16	2465	12	369	36	1688	19	1033	22	489	34	237	39	494	33	101	65	175	47	697	21	14502	5
	61-70 cm	940	22	698	27	445	28	915	21	1560	19	79	71	619	33	415	32	144	51	190	54	241	61	67	74	167	42	500	29	6981	8
	71-80 cm	199	56	227	45	128	59	564	36	708	28	107	59	405	39	226	72	136	58	39	100	41	100	30	100	118	70	170	41	3097	14
	81-90 cm	48	100	0	.	0	.	0	.	169	58	0	.	44	100	65	100	0	.	96	71	0	.	0	0	0	.	171	45	593	29
	91-100 cm	0	.	0	.	0	.	0	.	281	53	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	337	47
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14987	6	6063	12	4118	17	7138	11	10026	9	1823	23	7845	13	6528	13	2583	20	1624	24	2136	21	920	30	1310	27	6638	11	73741	3
maple	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	358	25	138	30	135	41	162	25	211	27	70	30	182	34	135	28	192	23	30	58	63	40	173	25	53	34	176	31	2078	8
	21-30 cm	636	15	269	25	103	37	131	34	774	21	264	26	338	26	507	21	201	37	123	41	218	42	170	30	89	41	91	39	3912	7
	31-40 cm	843	17	190	31	51	44	277	28	505	21	102	42	484	24	571	23	310	33	135	53	265	30	49	47	108	43	159	45	4048	8
	41-50 cm	669	16	155	39	30	75	78	43	270	27	100	38	207	31	297	26	101	51	89	44	180	32	84	74	65	54	52	49	2376	9
	51-60 cm	321	27	102	45	24	71	65	50	213	36	104	46	123	48	269	34	74	58	42	74	54	50	0	.	118	42	0	.	1509	13
	61-70 cm	59	71	0	.	30	100	0	.	203	47	0	.	34	100	100	86	95	52	30	100	0	.	0	.	0	.	0	.	551	28
	71-80 cm	0	.	72	72	0	.	0	.	34	100	19	100	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	152	46
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	27	100
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2885	10	925	19	373	25	713	18	2211	14	659	22	1368	17	1879	15	1000	18	449	28	831	25	475	26	432	30	479	25	14678	5
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	124	24	111	33	87	42	82	52	196	33	54	41	54	48	71	40	64	55	108	55	16	61	112	31	64	34	215	24	1358	10
	21-30 cm	258	33	138	35	167	38	113	37	203	29	219	45	195	48	318	32	74	50	71	42	69	44	137	39	114	74	315	33	2391	11
	31-40 cm	275	24	182	35	482	32	277	24	639	21	238	54	288	61	324	28	202	41	119	69	86	45	144	43	62	49	105	38	3423	10
	41-50 cm	431	25	155	38	400	34	345	36	631	19	86	59	189	37	387	34	70	56	112	61	57	50	54	85	0	0	106	61	3022	10
	51-60 cm	84	50	164	47	347	38	432	41	825	22	0	.	89	76	191	44	60	52	56	75	112	47	15	100	0	.	88	64	2464	13
	61-70 cm	157	52	81	70	60	71	194	45	311	33	0	.	32	100	154	59	72	71	0	.	28	100	0	.	0	.	55	100	1143	19
	71-80 cm	79	71	0	.	0	.	58	100	37	100	43	100	0	.	53	100	0	.	38	100	0	.	0	.	33	100	30	100	371	34
	81-90 cm	0	.	66	100	0	.	0	.	76	100	0	.	0	.	140	71	0	.	0	.	0	.	0	.	0	.	0	.	282	50
	91-100 cm	0	.	0	.	0	.	67	100	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	71
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1409	18	897	24	1542	25	1568	25	2970	15	640	38	847	38	1638	21	543	31	504	38	367	34	463	31	274	40	913	30	14573	7
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	98	46	34	78	70	50	6	72	19	42	1	100	0	0	0	.	14	84	2	100	2	100	61	35	3	100	94	42	405	20
	21-30 cm	285	39	53	50	244	39	92	43	50	48	12	100	33	62	7	100	19	68	0	0	0	.	98	39	6	100	303	24	1201	15
	31-40 cm	233	29	178	42	143	52	76	54	137	34	26	72	0	.	16	73	8	100	9	100	0	.	74	41	6	100	267	26	1175	14
	41-50 cm	329	32	228	41	238	36	178	34	337	32	24	100	91	66	12	100	8	100	37	74	0	.	20	76	22	100	220	31	1745	13
	51-60 cm	187	39	95	60	145	59	331	34	518	24	0	.	0	.	0	.	0	.	19	100	0	.	22	100	70	76	99	46	1488	15
	61-70 cm	86	74	34	100	166	50	131	50	265	54	0	.	0	0	0	.	0	.	0	.	0	.	0	.	32	100	34	100	747	26
	71-80 cm	0	.	114	71	67	75	0	.	267	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	457	34
	81-90 cm	0	.	0	.	0	.	0	.	215	51	0	.	0	.	45	100	0	.	37	100	0	.	0	.	0	.	0	.	298	42
	91-100 cm	0	.	0	.	0	.	44	100	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	71
	>100 cm	0	.	0	.	0	.	0	.	194	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	100
	total	1219	23	737	32	1073	28	857	22	2047	24	62	67	124	58	81	61	49	57	105	61	2	100	276	33	149	71	1017	20	7799	10
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	.	12	100	5	100	668	20	684	20
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	5	100	1410	16	1431	15
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	60	0	.	904	19	956	19
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	10	100	5	100	838	23	859	22
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	8	100	537	27	557	26
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	178	33	194	31
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	168	38	186	36
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	55	411	55
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	165	51	23	82	5412	13	5605	12
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	257	18	80	27	94	27	275	24	314	19	127	29	182	23	139	24	194	24	117	37	86	44	466	16	449	22	1181	12	3958	6
	21-30 cm	283	25	105	31	144	37	236	34	407	17	95	38	136	29	284	36	174	26	100	44	68	33	428	24	392	27	1679	16	4532	8
	31-40 cm	183	34	78	44	124	48	188	30	331	25	10	100	64	57	130	32	44	90	48	49	35	52	137	45	93	34	1246	18	2710	10
	41-50 cm	235	35	97	43	81	47	67	61	382	26	11	100	69	43	0	.	87	63	15	100	47	60	66	46	57	73	721	20	1936	11
	51-60 cm	36	73	95	68	233	55	115	54	120	44	0	.	26	100	92	51	140	73	22	100	31	71	54	60	42	71	104	42	1110	19
	61-70 cm	0	.	72	100	137	51	22	100	32	100	0	.	0	0	25	100	174	81	32	100	34	100	26	100	74	78	47	73	674	29
	71-80 cm	0	.	74	71	100	59	0	.	42	100	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	27	100	281	36
	81-90 cm	0	.	0	.	70	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100
	91-100 cm	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100
	>100 cm	0	.	0	.	63	100	66	100	94	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	223	59
	total	995	19	601	26	1101	29	969	20	1720	16	243	27	476	18	668	23	852	38	334	32	301	31	1175	19	1108	21	5004	11	15548	6
indeterminable	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	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	.	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	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	.	0	0
	51-60 cm	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
	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	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	.
	12-20 cm	3544	7	1010	14	1124	18	2290	12	2296	10	987	17	2706	10	1314	12	1431	12	781	19	680	19	2806	9	3236	8	4224	6	28430	3
	21-30 cm	7272	6	1776	13	2000	15	3427	12	4736	9	2466	15	5398	8	4078	11	2373	12	1643	16	1260	21	5207	9	6183	8	7961	6	55779	2
	31-40 cm	10916	6	2592	13	2720	14	4479	10	5958	8	3516	13	7135	8	6588	9	3712	11	2215	16	1622	18	6090	9	8120	7	8095	8	73757	2
	41-50 cm	12647	5	3282	13	3240	14	5271	10	7431	8	3696	13	8426	8	7246	9	4772	12	2507	18	2290	15	6351	9	10212	7	7221	7	84590	2
	51-60 cm	10131	6	2597	15	3155	15	6196	10	7665	8	3554	13	8447	8	7597	9	4099	12	1906	17	2224	17	5704	10	8852	8	5878	10	78004	2
	61-70 cm	5615	10	1504	18	1716	17	3956	13	4780	11	2256	19	4841	12	4765	13	2877	15	1277	22	1106	24	3307	13	5457	10	3164	10	46621	3
	71-80 cm	2531	14	867	27	770	26	1892	18	2776	15	892	26	2766	14	2397	18	1957	17	1122	29	1004	29	2118	18	2741	14	2040	17	25874	5
	81-90 cm	785	30	126	71	476	39	646	31	1497	22	320	46	1120	25	1062	25	341	39	269	43	343	37	798	27	1438	21	1263	23	10484	8
	91-100 cm	340	45	0	.	55	100	233	50	551	35	180	58	109	71	113	71	296	45	68	100	312	43	439	31	495	34	601	32	3792	12
	>100 cm	255	59	0	.	134	71	150	71	287	75	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	725	40	2719	19
	total	54037	4	13755	10	15390	11	28538	7	37979	5	17902	10	41210	6	35338	7	21905	9	11788	14	10932	13	32923	7	47182	5	41172	4	410050	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. 1320230. Birmensdorf, Swiss Federal Research Institute WSL

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