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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	.	0	.	52	100	0	.	638	53	0	.	0	.	5980	16	10113	14	1892	26	18675	9
	exposed wood core	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	47	69	0	.	0	.	1036	22	1251	17	233	36	2601	12
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	35	100	94	49	49	57	206	34
	stem breakage	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	18	100	0	.	0	.	222	32	299	23	79	72	635	18
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	74	0	.	0	.	25	76	7	100	0	.	58	48
	canker	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	138	47	14	71	159	41
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	.	97	60	0	.	0	.	474	27	605	25	185	50	1403	16
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	9	100	0	.	0	.	13	71	51	63	40	100	127	43
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	77	0	.	0	.	118	43	59	50	49	74	252	28
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	27	74	0	.	0	.	89	43	193	32	50	55	366	22
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	72	243	60	32	100	364	45
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	222	100	0	.	114	66	0	.	0	.	1212	23	1393	21	303	33	3244	15
	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	.	727	54	2574	37	241	100	1867	28	418	53	751	57	6661	21	6362	16	4059	19	23660	9
	exposed wood core	0	.	0	.	0	.	0	.	0	.	154	65	298	67	0	.	393	35	99	60	59	76	573	26	982	25	453	26	3010	13
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	48	0	.	7	100	106	57	22	58	14	70	209	33
	stem breakage	0	.	0	.	0	.	0	.	0	.	23	79	99	61	0	.	73	47	18	100	0	.	136	50	142	56	50	54	541	24
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	110	83	22	100	66	70	0	.	0	.	0	.	54	58	32	64	284	39
	canker	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	18	100	0	.	0	.	26	77	67	61	307	47	423	36
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	94	48	158	44	0	.	345	34	75	62	94	52	811	22	923	20	499	26	2999	11
	inclusion	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	26	100	0	.	7	100	41	70
	stag-headedness	0	.	0	.	0	.	0	.	0	.	55	100	0	.	9	100	42	61	0	.	0	.	115	68	62	52	85	47	367	30
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	25	77	33	81	0	.	115	36	50	43	117	37	383	22
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	72	100	22	100	0	.	0	.	0	.	35	100	0	.	18	100	147	58
	dead trees	0	.	0	.	0	.	0	.	0	.	186	62	454	57	136	100	935	41	66	90	104	65	1499	33	1181	23	371	31	4932	15
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	634	39	0	.	599	51	0	.	0	.	1849	35	3188	20	1369	36	2963	21	1160	42	684	44	5414	18	8863	15	6259	18	32979	7
	exposed wood core	22	58	0	.	13	100	0	.	0	.	500	33	504	34	74	46	872	28	214	47	82	50	920	22	1495	23	712	28	5411	10
	crack, woundwood rib	17	100	0	.	0	.	0	.	0	.	15	100	51	65	0	.	106	42	0	.	7	100	194	59	112	47	62	66	564	26
	stem breakage	19	100	0	.	33	70	0	.	0	.	71	57	134	52	30	71	173	40	55	100	0	.	73	50	155	31	214	40	958	17
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	63	80	0	.	0	.	39	56	22	75	22	58	154	38
	canker	39	61	0	.	0	.	0	.	0	.	38	85	92	52	0	.	69	47	37	100	0	.	25	76	155	47	52	54	506	22
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	21	74	0	.	74	75
	resinosis	21	73	0	.	14	71	0	.	0	.	397	35	539	27	8	100	706	27	128	46	108	60	756	25	1547	22	437	28	4662	11
	inclusion	19	100	0	.	0	.	0	.	0	.	219	75	47	76	0	.	33	81	17	100	0	.	44	85	62	60	7	100	449	40
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	55	55	22	100	53	72	0	.	0	.	36	71	81	56	34	71	281	28
	other type of damage	0	.	0	.	7	100	0	.	0	.	51	51	278	72	0	.	28	50	17	100	7	100	25	76	95	45	109	43	618	35
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	17	100	0	.	18	100	189	78	17	100	0	.	250	60
	dead trees	44	61	0	.	127	76	0	.	0	.	973	39	580	30	657	66	1336	31	147	55	194	54	1206	27	1339	23	484	27	7087	12
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5076	16	0	.	220	79	0	.	0	.	3714	24	3701	20	3489	20	3339	23	1510	34	1826	37	5393	20	3586	22	4903	18	36757	7
	exposed wood core	770	22	0	.	16	100	0	.	0	.	965	25	687	24	605	26	671	27	260	48	481	37	775	30	889	26	1577	22	7696	9
	crack, woundwood rib	103	47	0	.	0	.	0	.	0	.	0	.	55	57	48	73	18	72	7	100	55	100	18	100	35	71	94	45	434	24
	stem breakage	128	43	0	.	34	100	0	.	0	.	70	65	179	44	118	47	54	49	18	100	84	60	74	60	216	31	44	61	1021	16
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	21	77	77	49	0	.	57	63	0	.	18	100	18	100	46	54	45	71	282	26
	canker	257	35	0	.	0	.	0	.	0	.	39	69	198	38	86	47	76	47	65	59	48	66	100	53	135	40	127	39	1130	15
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	22	100	13	100	0	.	0	.	0	.	0	.	0	.	20	100	56	59
	resinosis	406	32	0	.	0	.	0	.	0	.	360	28	423	37	73	46	459	33	116	55	33	62	277	35	726	24	310	32	3183	11
	inclusion	8	100	0	.	0	.	0	.	0	.	159	80	19	73	169	57	16	100	18	100	0	.	7	100	32	83	140	41	568	30
	stag-headedness	51	57	0	.	0	.	0	.	0	.	33	71	74	47	49	73	24	77	36	71	0	.	43	71	48	58	107	46	465	20
	other type of damage	81	56	0	.	17	100	0	.	0	.	59	47	43	73	45	70	43	66	0	.	44	72	77	50	80	47	47	56	536	19
	lying living trees	8	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	35	100	171	71	23	100	0	.	54	100	306	46
	dead trees	403	29	0	.	25	76	0	.	0	.	1162	27	1119	38	786	24	502	28	226	46	518	39	684	25	1178	26	489	33	7093	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14210	10	0	.	589	78	174	81	217	63	3148	25	6654	16	5211	18	1799	33	2255	30	2592	38	3570	22	2228	28	5463	20	48111	6
	exposed wood core	1765	15	0	.	88	59	7	100	40	62	603	36	1775	20	977	24	492	34	817	33	605	32	752	32	836	28	1666	20	10423	8
	crack, woundwood rib	453	22	0	.	0	.	7	100	0	.	0	.	83	52	43	61	38	61	17	100	41	74	36	71	25	79	74	56	816	16
	stem breakage	174	39	0	.	0	.	0	.	0	.	118	42	245	32	80	45	106	53	33	71	72	62	43	73	142	47	187	29	1200	14
	defoliation >50%	140	54	0	.	0	.	17	100	0	.	57	64	21	77	0	.	91	53	0	.	0	.	0	.	26	100	14	70	365	28
	canker	160	33	0	.	16	100	0	.	20	74	13	71	300	29	119	40	45	83	42	71	59	88	64	59	141	50	193	38	1172	15
	bark necrosis	31	63	0	.	0	.	0	.	0	.	15	100	0	.	0	.	37	100	0	.	15	100	0	.	0	.	18	100	116	43
	resinosis	687	34	0	.	0	.	0	.	20	74	305	36	437	27	178	45	136	47	75	50	95	41	345	33	666	37	81	56	3026	14
	inclusion	131	35	0	.	0	.	0	.	0	.	18	100	101	46	156	62	0	.	31	100	53	87	0	.	54	69	112	58	655	23
	stag-headedness	95	44	0	.	0	.	0	.	17	100	15	100	0	.	30	71	35	71	18	100	15	100	37	71	24	77	144	66	431	27
	other type of damage	121	36	0	.	0	.	0	.	0	.	15	72	106	44	37	57	25	76	36	71	6	100	13	100	33	64	175	39	567	18
	lying living trees	19	100	0	.	0	.	0	.	0	.	15	100	33	71	0	.	35	100	0	.	35	100	83	82	17	100	0	.	237	39
	dead trees	1004	19	0	.	8	100	52	100	0	.	587	28	945	20	589	29	497	48	290	41	271	49	589	27	537	40	1043	26	6411	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	12935	11	1499	33	1697	38	2335	28	1237	42	1726	32	8400	15	8332	17	1316	28	1418	38	1393	30	3185	25	2821	26	5894	19	54188	6
	exposed wood core	3107	17	229	40	220	41	294	36	321	49	595	37	1486	22	1462	30	607	38	416	55	886	32	389	40	450	33	1598	20	12060	8
	crack, woundwood rib	199	31	17	100	32	71	0	.	7	100	19	100	60	63	36	67	46	58	0	.	0	.	17	100	18	100	18	100	469	20
	stem breakage	222	29	17	100	7	100	40	72	24	77	0	.	225	33	139	39	88	70	68	79	52	58	0	.	143	50	101	49	1125	15
	defoliation >50%	34	100	0	.	0	.	26	79	0	.	0	.	43	61	21	100	46	100	0	.	6	100	7	100	0	.	75	60	257	32
	canker	259	32	0	.	27	100	43	62	41	85	33	71	344	26	40	55	18	100	0	.	89	51	27	100	61	55	81	73	1064	15
	bark necrosis	133	66	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	58	76	213	47
	resinosis	391	31	7	100	106	49	43	61	23	77	84	49	473	39	256	32	90	69	65	70	105	59	257	44	357	36	106	52	2363	13
	inclusion	120	78	0	.	0	.	7	100	0	.	15	100	439	68	147	61	0	.	56	100	12	100	0	.	12	100	0	.	809	41
	stag-headedness	217	37	52	74	0	.	23	77	0	.	36	100	134	51	25	77	36	71	44	72	31	70	54	75	92	61	453	43	1198	20
	other type of damage	178	39	24	77	16	70	73	65	0	.	19	100	18	100	69	50	31	66	8	100	7	100	18	100	32	65	149	78	643	24
	lying living trees	17	100	17	100	0	.	18	100	17	100	0	.	28	79	17	100	37	100	0	.	0	.	16	100	61	58	63	77	291	29
	dead trees	745	21	185	40	124	49	205	45	101	47	110	68	929	25	735	28	383	37	374	48	374	37	517	38	464	38	1078	22	6324	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11477	12	3152	26	4627	18	8973	13	11622	13	955	47	6419	17	4953	20	1862	29	567	52	1498	39	1000	43	2501	24	5752	18	65357	5
	exposed wood core	2038	15	708	34	751	24	1180	22	1131	18	150	50	980	25	872	28	265	40	106	74	1064	69	425	46	513	32	2225	22	12410	9
	crack, woundwood rib	518	29	17	100	40	54	97	42	93	38	7	100	106	50	55	44	20	58	0	.	0	.	0	.	50	55	48	60	1052	17
	stem breakage	230	36	69	79	60	53	209	34	353	29	7	100	123	45	85	51	6	100	0	.	17	100	35	100	87	53	193	37	1476	14
	defoliation >50%	0	.	17	100	0	.	17	100	7	100	0	.	7	100	7	100	18	100	0	.	0	.	70	61	0	.	200	42	344	29
	canker	215	35	15	71	31	64	154	43	67	42	0	.	90	47	25	100	6	100	0	.	15	100	23	75	25	77	50	53	715	17
	bark necrosis	15	71	0	.	64	48	0	.	47	63	0	.	0	.	14	71	0	.	15	100	37	100	0	.	0	.	167	73	359	38
	resinosis	255	36	97	46	259	39	455	32	422	32	21	100	72	44	30	50	35	60	15	100	17	100	17	100	128	52	0	.	1824	14
	inclusion	88	44	0	.	7	100	37	71	18	100	0	.	63	100	7	100	0	.	0	.	0	.	18	100	0	.	43	85	283	32
	stag-headedness	279	33	69	61	52	52	53	58	119	46	0	.	39	71	40	71	0	.	15	100	15	100	17	100	91	83	264	43	1053	18
	other type of damage	170	36	21	75	107	35	129	43	120	33	39	71	129	46	41	67	30	66	18	100	0	.	22	57	75	57	124	37	1024	14
	lying living trees	0	.	0	.	0	.	7	100	23	77	36	100	0	.	7	100	0	.	0	.	0	.	54	74	25	76	0	.	151	40
	dead trees	1040	22	271	40	512	33	564	33	1161	21	89	100	390	33	601	28	182	37	0	.	205	55	252	47	292	31	1281	23	6839	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6774	17	8501	15	5915	18	12969	11	19930	8	0	.	952	49	1971	31	1071	54	782	59	188	71	1199	37	104	100	8026	17	68382	5
	exposed wood core	895	33	1517	18	588	27	1455	14	2887	13	0	.	17	100	733	58	87	93	150	56	6	100	237	53	0	.	2100	19	10672	8
	crack, woundwood rib	153	49	7	100	17	100	177	34	210	27	0	.	24	100	55	58	15	100	0	.	0	.	11	100	0	.	51	74	721	18
	stem breakage	62	66	150	32	82	47	217	26	441	27	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	138	36	1116	14
	defoliation >50%	19	100	161	58	17	100	65	53	61	52	0	.	0	.	27	100	0	.	0	.	0	.	25	100	0	.	466	44	841	28
	canker	24	77	113	40	0	.	228	31	258	35	0	.	0	.	0	.	96	100	0	.	0	.	32	100	0	.	41	61	792	20
	bark necrosis	7	100	80	88	0	.	41	61	44	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	71	211	39
	resinosis	345	37	365	33	216	44	850	23	1339	17	0	.	31	100	8	100	0	.	0	.	0	.	35	100	0	.	0	.	3190	11
	inclusion	42	85	55	100	64	68	0	.	40	61	0	.	0	.	25	100	81	100	0	.	0	.	8	100	0	.	102	50	417	31
	stag-headedness	35	70	198	35	96	46	31	64	244	31	0	.	0	.	42	100	0	.	0	.	15	100	0	.	0	.	648	26	1308	16
	other type of damage	45	49	38	69	68	47	99	46	232	26	0	.	0	.	42	70	0	.	0	.	12	100	0	.	0	.	184	34	719	16
	lying living trees	20	100	7	100	40	61	7	100	109	49	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	83	62	284	29
	dead trees	643	24	627	26	814	29	613	24	1140	17	0	.	36	65	258	49	54	77	52	76	37	100	184	47	0	.	2090	21	6548	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	51106	4	13151	11	13648	11	24452	8	33006	6	12119	12	31940	7	25564	8	14853	10	8112	15	8932	16	32403	7	36576	6	42247	5	348109	2
	exposed wood core	8596	8	2454	15	1675	15	2937	11	4379	10	2969	15	5782	10	4724	15	3434	13	2062	20	3184	26	5107	10	6417	9	10563	7	64283	3
	crack, woundwood rib	1443	14	42	61	89	40	281	26	309	22	42	61	379	24	237	27	332	22	25	76	111	58	417	33	355	24	410	22	4470	7
	stem breakage	835	17	236	32	217	30	466	20	818	19	290	28	1022	17	479	21	518	23	193	44	225	34	583	21	1183	14	1008	15	8074	5
	defoliation >50%	193	44	178	53	17	100	125	37	68	48	78	51	267	39	77	53	368	29	0	.	25	79	183	33	155	33	853	27	2586	12
	canker	954	16	128	36	74	50	424	24	386	26	129	38	1024	15	270	26	328	36	143	42	212	37	303	28	721	20	865	21	5962	7
	bark necrosis	186	49	80	88	64	48	41	61	92	40	15	100	96	63	28	60	37	100	15	100	52	76	0	.	21	74	302	45	1029	20
	resinosis	2104	16	470	27	595	25	1349	18	1804	14	1262	17	2174	14	553	22	1869	15	474	24	453	25	2973	11	4951	10	1618	14	22650	4
	inclusion	409	29	55	100	71	62	44	61	59	52	417	50	684	45	504	33	139	63	122	56	65	73	118	44	211	32	450	25	3349	14
	stag-headedness	677	20	319	28	148	35	107	38	379	25	139	51	303	28	216	33	216	30	114	42	77	44	420	27	458	26	1784	17	5355	8
	other type of damage	594	19	83	43	214	25	301	28	351	21	183	28	625	35	233	28	209	26	112	40	77	47	358	21	560	18	954	18	4855	7
	lying living trees	65	52	24	77	40	61	32	65	149	40	51	76	148	54	55	55	90	60	51	75	223	57	487	38	364	42	251	38	2030	16
	dead trees	3878	9	1083	19	1610	19	1433	18	2401	13	3107	17	4676	14	3763	16	4002	16	1155	23	1703	19	6144	12	6383	10	7140	8	48478	4
	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. 1426310. Birmensdorf, Swiss Federal Research Institute WSL

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