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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NFI1
growing stock (stemwood)
main tree species · diameter at breast height (main diameter classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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 (main diameter classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.
	12-15 cm	338	13	86	29	110	25	302	19	430	14	245	15	361	11	231	15	174	13	83	21	131	22	294	11	488	8	118	18	3391	4
	16-23 cm	1146	11	266	27	528	18	697	15	1012	12	965	13	1375	10	1058	12	669	13	349	21	450	20	1202	11	1865	8	443	14	12024	3
	24-35 cm	3828	8	741	25	2264	15	2423	12	4034	10	2674	12	4727	8	3749	11	2634	12	1209	18	1491	17	3346	10	5949	8	1451	14	40522	3
	36-51 cm	7309	6	1356	19	3421	12	6056	10	8049	8	4508	11	9214	7	6790	8	4920	10	1730	16	2621	15	4035	8	9848	6	2678	11	72536	2
	=52 cm	4986	9	549	26	1756	16	3346	12	3805	11	2503	14	5770	9	5185	12	3471	12	1496	21	1882	19	3008	12	7680	8	2558	13	47994	3
	total	17607	5	2999	15	8079	11	12825	8	17331	7	10895	9	21446	6	17013	7	11867	9	4867	14	6575	12	11884	7	25830	5	7248	9	176466	2
fir	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	289	10	26	29	74	27	168	19	88	20	64	24	190	12	79	17	19	36	32	36	25	56	53	33	18	26	16	36	1140	6
	16-23 cm	1017	9	77	35	164	26	606	17	367	15	218	22	697	12	420	15	93	24	114	33	87	39	117	27	70	27	55	29	4103	5
	24-35 cm	3218	8	359	29	457	27	1831	14	1311	13	638	22	2057	12	1332	14	210	24	240	28	152	28	210	28	160	32	186	49	12361	4
	36-51 cm	4488	7	498	21	637	23	2664	11	2932	12	1162	15	3993	9	2447	12	524	23	329	33	354	34	360	27	424	26	440	23	21250	4
	=52 cm	2772	11	159	34	364	27	1535	17	1008	18	802	25	3678	12	1094	18	471	26	294	38	303	48	265	32	492	28	596	28	13833	5
	total	11784	6	1117	21	1696	19	6803	10	5707	10	2884	15	10616	8	5371	10	1318	18	1009	24	921	26	1005	21	1164	19	1293	20	52687	3
pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	13	47	22	64	10	82	9	54	7	45	0	.	18	45	1	100	5	73	10	43	4	100	136	19	121	20	2	100	357	12
	16-23 cm	69	34	12	70	22	47	20	50	44	34	5	100	32	45	7	68	13	62	41	48	10	71	413	17	341	18	14	50	1044	10
	24-35 cm	473	21	271	26	233	40	141	40	520	23	0	.	104	39	19	100	8	127	186	38	55	62	654	19	841	17	178	47	3681	8
	36-51 cm	980	16	442	24	413	31	488	22	1730	13	0	.	219	51	148	41	92	54	77	53	44	66	609	20	1045	15	140	42	6426	7
	=52 cm	244	29	127	43	136	43	178	49	661	21	0	.	89	64	42	72	14	100	0	.	0	.	164	56	370	29	20	71	2045	12
	total	1778	14	873	20	814	27	836	22	2961	12	5	100	462	39	218	37	131	44	313	32	112	63	1977	15	2718	13	352	37	13553	6
larch	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	1	100	4	81	1	100	2	100	17	64	0	.	0	.	1	100	0	0	0	.	7	74	98	19	32	22	40	25	204	13
	16-23 cm	15	100	6	77	33	87	16	44	34	47	2	100	0	.	0	.	10	64	0	.	7	67	374	15	215	17	244	26	957	10
	24-35 cm	12	72	58	46	84	65	29	100	113	41	0	.	0	.	0	.	27	79	0	.	122	69	1205	13	801	16	677	17	3129	8
	36-51 cm	9	100	68	54	115	42	52	60	300	25	32	100	37	65	72	56	77	52	8	100	135	51	2290	10	1901	11	1295	11	6391	6
	=52 cm	45	59	25	71	72	57	28	75	246	35	0	.	80	71	72	51	117	46	29	100	95	59	2515	11	1711	15	1116	16	6152	7
	total	82	43	161	40	304	41	129	42	710	20	34	100	117	68	145	45	232	33	37	100	366	42	6482	8	4660	10	3373	10	16833	5
Arolla pine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	1	100	0	.	18	35	29	26	0	.	49	20
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	8	100	4	100	0	.	2	100	0	.	102	26	86	21	1	100	202	16
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	2	100	0	.	215	25	318	21	0	.	542	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	4	100	4	100	0	.	189	23	450	22	3	157	655	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	146	32	367	22	0	.	520	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	21	100	11	100	4	100	8	100	0	.	670	17	1251	16	4	135	1969	12
other conifers	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	8	39	5	70	5	100	8	43	10	51	0	.	3	69	2	60	0	.	3	63	1	71	2	100	8	52	0	.	57	20
	16-23 cm	14	72	23	64	7	100	21	60	24	37	0	.	0	.	4	76	1	100	0	.	1	100	0	.	14	52	0	.	110	24
	24-35 cm	4	100	0	.	16	72	39	79	48	63	0	.	0	.	0	.	0	.	0	.	-2	100	6	100	3	100	0	.	113	40
	36-51 cm	0	.	0	.	73	93	55	58	62	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	190	49
	=52 cm	0	.	16	100	58	62	75	79	25	73	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	39
	total	26	62	44	54	159	59	198	47	170	52	0	.	28	88	7	54	1	100	3	63	0	1233	9	77	26	47	0	.	670	24
beech	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	470	10	221	14	145	18	184	15	326	11	64	27	230	15	195	16	79	29	37	29	53	25	71	34	37	27	321	13	2432	4
	16-23 cm	1867	8	711	14	491	16	760	13	944	10	121	25	746	12	577	15	223	21	196	41	235	26	144	26	115	26	730	14	7862	4
	24-35 cm	5105	7	1678	12	1268	16	1900	12	2186	10	651	21	1599	12	1971	13	469	19	235	26	566	22	107	43	227	29	826	13	18787	3
	36-51 cm	4902	7	2339	12	1269	15	2885	11	3062	9	610	22	2205	13	1956	12	677	22	332	26	657	21	45	55	336	39	786	13	22061	3
	=52 cm	1258	15	1166	21	624	30	1545	17	1973	14	266	27	990	20	559	22	469	27	88	42	217	41	53	65	231	46	495	21	9934	6
	total	13602	5	6116	9	3797	12	7274	9	8492	7	1711	18	5770	10	5258	10	1917	16	889	21	1727	18	420	24	946	27	3158	9	61077	3
maple	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	88	14	40	27	30	56	18	38	61	24	28	26	36	22	52	20	60	26	14	36	31	37	27	28	10	40	11	36	505	8
	16-23 cm	389	13	123	23	84	35	108	29	287	18	109	25	148	19	226	18	132	23	42	34	115	31	33	36	23	39	44	37	1863	6
	24-35 cm	720	12	282	21	84	36	227	27	436	19	111	41	238	24	397	17	220	22	61	36	190	26	37	39	23	60	51	34	3077	6
	36-51 cm	354	16	169	25	58	41	66	49	370	21	68	35	114	35	217	25	97	29	31	46	96	27	5	100	29	48	20	74	1695	8
	=52 cm	19	134	131	39	11	100	55	67	102	47	16	71	20	71	66	54	29	58	0	.	25	59	0	.	8	100	9	100	491	20
	total	1570	9	745	15	267	25	475	22	1256	12	331	22	557	17	958	13	538	16	148	26	457	21	101	27	93	31	136	28	7630	4
ash	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	81	19	21	33	42	30	35	37	83	24	29	34	53	31	72	25	27	36	10	43	20	35	25	33	17	44	61	20	576	8
	16-23 cm	315	20	133	23	239	23	106	25	357	17	107	36	163	30	273	19	48	44	46	36	51	53	23	46	42	60	123	24	2026	7
	24-35 cm	510	18	267	23	320	23	273	27	770	15	126	46	221	24	508	25	80	35	64	49	63	53	80	36	38	46	45	53	3363	7
	36-51 cm	347	23	178	26	288	27	270	25	901	14	59	49	115	31	253	26	35	55	43	76	70	48	7	100	6	100	32	65	2604	8
	=52 cm	82	38	102	53	113	51	168	50	243	30	13	100	12	100	58	59	12	100	0	.	7	100	0	.	15	100	0	.	825	18
	total	1335	14	701	17	1002	18	851	20	2354	10	335	28	563	18	1164	16	202	29	163	37	210	36	134	29	118	43	261	20	9394	5
oak	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	54	29	13	54	59	40	19	49	26	37	5	82	0	100	2	100	3	62	1	137	0	0	14	28	1	105	61	34	259	15
	16-23 cm	185	26	68	29	164	32	69	26	161	23	9	86	12	57	8	58	39	49	0	.	0	.	38	35	7	66	156	21	917	10
	24-35 cm	422	23	225	27	259	23	183	26	407	23	12	100	12	80	23	80	40	61	5	100	0	.	11	59	31	85	243	28	1875	10
	36-51 cm	285	27	225	26	464	23	365	19	537	20	19	82	20	58	7	71	16	72	13	71	0	.	10	115	68	55	147	35	2178	9
	=52 cm	184	45	255	36	291	37	214	31	741	20	15	100	12	100	95	47	0	.	11	100	0	.	0	.	11	100	15	156	1844	13
	total	1130	18	785	19	1237	18	851	16	1872	13	60	44	57	46	135	38	98	49	31	58	0	0	74	31	118	56	623	22	7073	6
sweet chestnut	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	1	100	0	.	336	13	338	13
	16-23 cm	0	.	0	.	18	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	7	71	2	100	924	12	952	12
	24-35 cm	0	.	0	.	19	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	58	0	.	723	13	766	13
	36-51 cm	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	6	100	582	17	596	17
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	74	16	100	1197	21	1230	21
	total	0	.	0	.	41	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	52	46	24	74	3763	10	3883	10
other broadleaves	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	138	15	23	25	56	32	45	31	138	16	41	22	87	25	89	25	65	22	46	40	25	44	176	16	88	19	420	11	1437	5
	16-23 cm	265	14	132	21	134	26	108	25	380	14	54	25	153	23	229	23	122	21	37	29	95	31	222	17	133	22	869	9	2935	5
	24-35 cm	431	16	262	31	261	30	130	28	474	17	28	61	116	48	154	28	155	29	59	51	113	33	171	30	103	40	887	14	3345	7
	36-51 cm	171	27	172	33	107	39	96	31	354	21	0	.	82	36	74	33	107	40	40	56	40	53	79	49	3	162	230	20	1556	9
	=52 cm	16	100	132	49	37	58	80	52	114	49	0	.	0	.	19	100	70	47	0	.	0	.	43	90	0	.	60	43	571	20
	total	1021	11	721	25	596	23	460	20	1460	13	123	21	437	20	564	18	519	21	183	29	274	25	691	19	327	21	2466	9	9843	5
indeterminable	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	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	.
	12-15 cm	1480	5	460	11	533	12	790	10	1186	7	474	11	981	7	725	9	433	11	236	16	297	17	914	8	849	7	1386	6	10746	2
	16-23 cm	5283	5	1552	10	1885	10	2512	8	3610	6	1591	11	3333	6	2807	8	1350	10	827	16	1053	14	2675	7	2916	7	3603	5	34995	2
	24-35 cm	14722	4	4144	9	5266	9	7176	7	10298	6	4240	10	9081	6	8153	7	3843	10	2062	13	2749	12	6067	7	8493	6	5269	6	91562	2
	36-51 cm	18845	4	5446	9	6849	9	12999	7	18298	5	6458	9	16004	6	11964	6	6549	9	2608	14	4016	12	7632	6	14116	5	6353	6	138137	1
	=52 cm	9606	6	2661	13	3462	13	7224	9	8919	7	3615	12	10675	7	7197	10	4654	10	1919	18	2529	16	6210	8	10901	7	6066	8	85639	2
	total	49936	3	14262	7	17994	7	30702	5	42312	4	16378	8	40075	5	30845	6	16828	7	7652	11	10644	10	23499	5	37275	4	22677	4	361078	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. 1400605. Birmensdorf, Swiss Federal Research Institute WSL

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