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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NFI3
total number of stems with two most important forms of damages
altitude (in 200 m classes) · type of damage to trees (13 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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
altitude (in 200 m classes)	type of damage to trees (13 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	exposed wood core	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	stem breakage	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	canker	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	257	83	318	63	0	.	652	36	83	72	122	72	7383	11	11066	9	2932	14	22813	6
	exposed wood core	0	.	0	.	0	.	0	.	0	.	0	.	70	56	0	.	76	45	69	100	0	.	1157	20	1494	13	431	18	3298	10
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	16	100	11	100	0	.	21	100	85	36	67	36	204	24
	stem breakage	0	.	0	.	0	.	0	.	0	.	18	100	15	77	0	.	20	71	11	100	10	100	261	20	352	20	131	34	817	12
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	74	0	.	0	.	31	49	28	55	10	100	84	31
	canker	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	38	41	178	31	44	53	265	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	84	73	29	87	0	.	91	46	11	100	0	.	982	29	970	16	253	27	2421	14
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	13	74	0	.	5	100	0	.	4	100	8	71	38	53	22	100	90	36
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	63	11	100	10	100	86	36	113	30	32	67	281	20
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	25	60	0	.	0	.	110	29	203	26	73	34	414	16
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	50	350	45	62	51	494	33
	dead trees	0	.	0	.	0	.	0	.	0	.	43	100	155	87	0	.	163	39	0	.	0	.	1174	17	2035	17	349	23	3920	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	599	42	1945	28	226	71	2269	20	390	49	935	36	7246	14	10213	11	4313	14	28137	6
	exposed wood core	0	.	0	.	0	.	0	.	0	.	188	48	255	49	14	100	381	24	54	60	77	44	778	18	1332	15	600	20	3679	9
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	6	100	13	75	0	.	44	44	0	.	32	53	84	45	84	36	65	36	328	18
	stem breakage	0	.	0	.	0	.	0	.	0	.	24	62	79	47	0	.	74	35	10	100	4	100	162	35	216	28	68	37	638	16
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	62	82	11	100	50	55	0	.	0	.	14	77	74	41	27	54	238	29
	canker	0	.	0	.	0	.	0	.	0	.	24	64	0	.	0	.	10	100	0	.	9	100	52	40	144	30	178	44	416	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	106	41	199	27	34	100	372	23	56	50	109	37	967	16	1629	14	459	20	3929	8
	inclusion	0	.	0	.	0	.	0	.	0	.	8	71	13	100	0	.	0	.	0	.	0	.	49	57	4	100	29	56	102	34
	stag-headedness	0	.	0	.	0	.	0	.	0	.	32	100	9	100	4	100	34	52	0	.	0	.	62	68	123	35	97	40	361	22
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	46	55	0	.	33	52	18	81	10	100	152	25	107	26	91	30	456	14
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	44	100	11	100	103	100	0	.	0	.	21	100	94	49	10	100	282	44
	dead trees	0	.	0	.	0	.	0	.	0	.	172	43	448	38	66	100	692	33	65	67	145	41	1386	22	1565	14	470	21	5009	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	693	26	0	.	436	41	0	.	0	.	2437	23	3697	17	1410	24	3502	15	950	31	781	33	5919	13	8491	11	6651	12	34968	5
	exposed wood core	61	39	0	.	18	72	0	.	0	.	519	24	507	24	71	38	871	19	232	32	80	37	856	16	1325	18	925	19	5464	8
	crack, woundwood rib	37	44	0	.	0	.	0	.	0	.	14	73	39	52	0	.	76	38	0	.	4	100	111	60	121	42	66	41	467	21
	stem breakage	11	100	0	.	18	71	0	.	0	.	84	46	133	37	32	52	154	32	54	62	9	100	162	34	152	25	227	26	1035	12
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	14	77	27	54	0	.	106	41	0	.	0	.	30	49	82	69	27	50	285	27
	canker	43	41	0	.	0	.	0	.	0	.	46	55	63	45	10	100	46	41	24	86	0	.	36	48	89	45	89	31	447	16
	bark necrosis	4	100	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	12	75	0	.	42	66
	resinosis	102	37	0	.	18	66	0	.	0	.	349	27	528	25	5	100	660	21	112	36	111	44	766	17	1431	15	446	19	4528	8
	inclusion	11	100	0	.	0	.	0	.	0	.	171	56	28	76	0	.	63	50	10	100	0	.	36	66	75	40	23	61	417	27
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	34	49	11	100	52	51	0	.	0	.	40	50	76	46	103	35	315	20
	other type of damage	0	.	0	.	4	100	0	.	0	.	48	40	165	71	0	.	51	39	14	78	4	100	22	54	104	45	107	30	518	26
	lying living trees	0	.	0	.	0	.	0	.	0	.	21	71	32	100	4	100	145	59	0	.	10	100	156	55	10	100	42	63	420	31
	dead trees	62	40	0	.	80	66	0	.	0	.	871	28	680	23	578	44	1263	22	129	42	446	36	1182	20	1077	17	641	18	7008	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6285	12	0	.	291	56	98	100	0	.	4051	18	3997	15	3576	14	3631	18	2039	24	2120	25	5256	14	5129	15	6649	12	43122	5
	exposed wood core	931	16	0	.	12	77	0	.	0	.	845	20	833	19	712	27	563	22	369	41	466	29	877	20	898	18	1825	15	8331	6
	crack, woundwood rib	99	34	0	.	0	.	0	.	0	.	32	74	47	45	37	58	10	72	4	100	37	90	39	50	44	46	73	38	421	17
	stem breakage	132	30	0	.	23	84	20	100	0	.	84	40	270	28	103	37	88	31	31	75	69	47	128	36	169	26	194	30	1311	11
	defoliation >50%	11	100	0	.	0	.	0	.	0	.	17	63	46	46	0	.	74	44	0	.	16	73	34	67	36	48	44	51	277	20
	canker	231	25	0	.	0	.	0	.	0	.	74	41	141	32	98	35	65	37	41	54	54	46	71	45	142	28	97	35	1013	11
	bark necrosis	8	71	0	.	0	.	0	.	0	.	11	100	11	100	8	100	0	.	0	.	10	100	0	.	0	.	11	100	59	40
	resinosis	470	20	0	.	0	.	4	100	0	.	580	32	467	23	177	47	406	25	130	33	72	36	325	26	769	17	250	24	3651	9
	inclusion	35	43	0	.	0	.	0	.	0	.	220	53	86	46	130	47	14	76	10	100	0	.	47	52	37	65	105	34	685	22
	stag-headedness	55	42	0	.	0	.	0	.	0	.	19	71	71	36	45	51	24	61	28	58	0	.	76	48	68	38	178	30	565	15
	other type of damage	92	36	0	.	10	100	0	.	0	.	52	38	43	54	44	45	43	48	23	61	24	72	63	47	88	32	111	33	593	14
	lying living trees	4	100	0	.	0	.	0	.	0	.	0	.	34	52	0	.	20	71	51	60	119	59	86	44	0	.	124	38	438	23
	dead trees	389	21	0	.	14	76	0	.	0	.	919	23	1101	25	742	19	623	24	276	34	512	28	797	18	1074	19	1161	24	7608	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15244	7	38	100	516	56	115	75	204	54	3688	17	6958	12	6258	13	2690	19	2050	22	2156	29	4229	15	3547	17	8095	12	55789	4
	exposed wood core	1938	12	20	100	65	50	4	100	38	47	828	21	1591	16	1135	19	681	23	678	26	543	26	1017	23	926	19	2247	12	11712	6
	crack, woundwood rib	408	19	0	.	0	.	4	100	0	.	37	64	70	42	30	53	50	39	29	58	28	66	20	71	63	41	89	36	829	12
	stem breakage	208	27	0	.	0	.	0	.	0	.	122	33	211	27	124	34	105	35	22	61	98	49	96	42	181	31	232	22	1399	10
	defoliation >50%	106	42	0	.	0	.	10	100	0	.	76	42	30	67	0	.	72	42	0	.	0	.	29	74	65	55	67	37	456	18
	canker	183	24	0	.	9	100	0	.	22	61	20	45	311	22	137	32	88	48	38	53	48	68	61	43	137	41	183	30	1236	11
	bark necrosis	33	49	0	.	0	.	0	.	0	.	30	76	0	.	0	.	20	100	0	.	9	100	0	.	0	.	76	43	168	29
	resinosis	674	23	0	.	4	100	0	.	12	74	317	30	486	19	150	36	148	32	141	46	125	41	324	25	696	24	175	34	3253	9
	inclusion	97	29	0	.	11	100	0	.	4	100	48	48	168	46	163	42	34	68	31	66	47	62	37	60	64	52	136	36	838	16
	stag-headedness	116	33	10	100	0	.	0	.	10	100	29	58	56	58	40	46	34	52	22	71	9	100	41	50	91	46	161	37	620	16
	other type of damage	140	29	0	.	11	100	0	.	0	.	31	44	89	36	41	45	43	56	32	58	22	62	8	100	30	44	228	24	675	13
	lying living trees	21	70	0	.	0	.	0	.	0	.	9	100	43	54	11	100	23	85	12	100	21	100	136	54	20	71	29	58	324	27
	dead trees	1092	14	0	.	9	70	29	100	0	.	717	21	859	17	519	22	398	35	244	31	262	34	758	24	625	24	1262	18	6773	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11869	9	1748	23	2429	24	1816	25	1682	33	2319	21	8189	11	8291	12	1938	34	1160	31	1883	22	4094	17	2672	19	6450	13	56541	4
	exposed wood core	3151	12	239	32	254	31	248	32	361	35	455	30	1464	17	1654	19	504	29	337	43	1023	23	571	26	498	26	2083	16	12843	6
	crack, woundwood rib	197	21	15	73	31	52	10	100	14	77	25	61	44	52	37	49	28	58	10	100	17	80	10	100	10	100	25	62	472	14
	stem breakage	186	25	10	100	28	74	24	72	27	54	46	51	195	25	195	38	52	65	44	74	57	55	36	68	90	45	87	38	1078	12
	defoliation >50%	31	73	0	.	0	.	15	78	0	.	0	.	29	54	22	71	28	100	0	.	8	71	14	77	0	.	53	49	199	26
	canker	214	24	24	71	32	71	54	62	28	73	28	54	402	28	23	55	50	49	0	.	86	41	15	100	60	41	67	52	1082	14
	bark necrosis	92	56	0	.	0	.	0	.	0	.	0	.	19	71	26	100	0	.	0	.	0	.	0	.	0	.	114	58	251	35
	resinosis	349	22	34	89	108	37	25	61	42	49	196	34	369	31	294	29	96	44	121	60	110	49	182	36	298	27	77	46	2299	10
	inclusion	204	55	51	62	27	73	25	61	0	.	9	100	298	58	116	49	0	.	59	67	38	83	0	.	21	61	62	58	910	25
	stag-headedness	191	27	39	61	10	100	33	66	0	.	21	100	78	51	40	54	29	58	25	72	41	50	61	53	51	60	347	33	966	15
	other type of damage	135	32	18	64	12	58	50	55	11	100	25	56	37	51	114	47	18	64	5	100	14	77	29	58	31	49	153	45	651	17
	lying living trees	30	74	10	100	0	.	10	100	10	100	10	100	45	46	15	77	20	100	0	.	0	.	9	100	43	50	59	61	261	23
	dead trees	799	16	149	36	118	42	194	38	144	37	264	33	821	18	749	20	301	30	298	39	312	32	662	24	451	27	1035	22	6295	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	12476	8	3250	18	6075	13	8559	10	10929	10	965	35	5545	13	5360	14	1916	26	810	33	1144	34	1778	23	2003	22	5632	13	66441	4
	exposed wood core	2121	11	627	24	984	15	922	18	1216	15	113	43	1063	19	795	22	379	42	245	46	884	53	451	30	382	28	2021	17	12204	6
	crack, woundwood rib	441	22	50	47	36	41	59	40	83	31	4	100	77	40	43	41	34	58	0	.	0	.	0	.	28	55	47	46	901	13
	stem breakage	209	27	49	67	78	35	149	30	306	22	16	78	157	30	85	42	22	85	0	.	23	62	44	56	97	45	157	30	1393	10
	defoliation >50%	0	.	19	71	9	100	20	71	4	100	0	.	19	63	4	100	30	74	0	.	0	.	68	55	0	.	241	36	415	24
	canker	171	26	12	58	35	51	125	33	101	28	0	.	105	32	19	80	16	80	30	100	22	72	13	76	13	77	44	40	708	13
	bark necrosis	8	71	0	.	49	40	0	.	31	56	0	.	0	.	8	71	0	.	9	100	20	100	0	.	0	.	123	60	249	33
	resinosis	213	28	71	40	282	27	362	24	369	23	33	64	161	33	53	42	21	60	32	51	10	100	67	51	87	44	0	.	1761	10
	inclusion	92	35	8	71	4	100	20	71	38	58	7	100	63	72	8	71	18	100	9	100	0	.	16	73	0	.	58	46	343	21
	stag-headedness	265	25	68	47	70	36	36	52	100	35	0	.	97	59	54	45	20	100	9	100	9	100	24	61	60	71	290	30	1103	13
	other type of damage	138	30	29	52	121	26	97	36	120	34	26	62	121	35	53	42	51	52	29	75	0	.	26	60	54	47	144	27	1011	11
	lying living trees	0	.	0	.	0	.	15	78	23	61	21	100	0	.	4	100	0	.	0	.	4	100	46	54	14	76	0	.	126	31
	dead trees	1261	14	331	29	507	23	472	25	949	16	104	62	459	25	533	26	230	39	101	76	150	46	426	28	247	27	1385	18	7155	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6214	12	8488	11	6314	13	12642	8	23140	6	0	.	1381	31	2125	23	975	40	741	41	211	53	1163	30	298	51	7864	11	71557	3
	exposed wood core	954	22	1289	14	765	26	1425	12	2932	9	0	.	65	50	460	54	138	73	146	42	76	83	414	46	16	100	2123	13	10801	6
	crack, woundwood rib	260	31	10	73	69	41	149	27	209	20	0	.	15	100	51	52	8	100	20	100	0	.	6	100	0	.	45	51	843	13
	stem breakage	92	35	129	29	130	35	229	25	351	21	0	.	21	100	27	71	20	70	0	.	0	.	0	.	0	.	157	39	1156	11
	defoliation >50%	21	71	113	47	13	78	65	40	38	48	0	.	21	71	15	100	0	.	0	.	0	.	13	100	0	.	398	32	698	21
	canker	63	38	99	30	34	58	232	26	216	26	0	.	0	.	0	.	69	79	0	.	0	.	17	100	0	.	55	42	785	14
	bark necrosis	4	100	56	70	8	71	23	61	29	45	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	74	43	198	27
	resinosis	281	28	308	26	290	36	879	18	1372	13	0	.	28	73	16	77	0	.	13	74	0	.	29	74	0	.	22	100	3238	9
	inclusion	46	52	54	72	70	59	44	54	64	52	0	.	0	.	15	100	44	100	4	100	0	.	30	72	0	.	87	42	458	21
	stag-headedness	58	53	188	31	93	35	82	45	211	24	0	.	25	61	35	74	0	.	0	.	9	100	0	.	0	.	596	21	1298	12
	other type of damage	67	36	72	49	112	31	141	30	217	19	0	.	0	.	23	70	0	.	9	100	7	100	10	100	0	.	172	28	830	12
	lying living trees	15	79	4	100	47	52	8	71	90	41	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	68	48	242	24
	dead trees	581	19	576	21	792	21	535	18	1247	15	0	.	129	57	267	39	49	54	78	53	22	100	300	37	20	100	2241	16	6838	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52781	3	13523	8	16061	8	23229	6	35954	5	14316	9	32031	5	27247	6	17574	8	8224	11	9352	11	37069	5	43419	5	48586	3	379366	1
	exposed wood core	9155	6	2175	11	2098	12	2599	9	4547	7	2949	11	5848	8	4841	10	3594	10	2132	15	3149	18	6120	8	6870	7	12255	5	68331	2
	crack, woundwood rib	1442	11	75	36	137	26	222	21	306	17	118	33	309	19	199	23	266	19	75	41	118	37	291	29	434	17	476	14	4465	5
	stem breakage	838	12	187	27	277	22	422	18	684	15	394	19	1081	12	565	18	535	16	172	32	270	25	889	13	1257	11	1254	11	8826	4
	defoliation >50%	168	32	132	41	22	61	111	30	42	44	107	33	234	28	52	47	376	21	0	.	23	54	233	24	285	27	866	18	2652	9
	canker	906	11	135	26	110	33	411	20	367	18	191	24	1027	14	288	21	344	23	133	36	220	26	304	19	762	14	755	15	5952	5
	bark necrosis	149	36	56	70	57	36	23	61	60	36	40	62	56	55	47	60	20	100	9	100	39	62	0	.	12	75	398	27	967	15
	resinosis	2089	10	412	22	702	19	1270	14	1795	11	1665	15	2268	10	729	19	1794	11	615	19	537	19	3643	10	5879	7	1682	10	25082	3
	inclusion	485	25	113	45	112	42	89	35	106	38	462	33	669	30	433	25	179	35	123	39	88	48	222	25	239	23	520	17	3841	9
	stag-headedness	687	14	305	23	172	25	151	31	321	19	101	43	371	23	228	23	220	23	96	34	79	35	391	20	582	16	1804	11	5508	6
	other type of damage	573	14	118	34	270	18	288	21	347	17	182	20	504	27	275	24	263	20	130	29	82	34	420	15	617	14	1080	11	5149	5
	lying living trees	70	42	14	77	47	52	33	50	123	33	61	47	198	33	44	44	311	44	73	47	153	48	534	24	531	31	394	21	2587	11
	dead trees	4183	7	1056	15	1519	14	1229	14	2341	10	3091	12	4653	10	3454	11	3719	12	1192	17	1849	15	6684	8	7094	7	8543	7	50607	3
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

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. 1426315. Birmensdorf, Swiss Federal Research Institute WSL

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