Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
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 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
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	.	296	82	375	60	89	100	851	39	193	75	121	71	7331	11	12669	9	3885	14	25810	6
	exposed wood core	0	.	0	.	0	.	0	.	0	.	7	100	41	56	20	100	63	45	38	100	0	.	1152	13	1617	12	684	17	3623	8
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	11	100	0	.	20	71	131	51	94	30	266	28
	stem breakage	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	4	100	0	.	10	100	86	33	171	27	107	40	387	18
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	71	0	.	0	.	29	74	14	78	11	74	74	39
	canker	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	11	100	0	.	28	58	129	42	16	50	188	31
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	46	71	10	100	0	.	125	42	105	75	4	100	1085	16	1281	14	349	25	3007	9
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	6	100	0	.	0	.	68	37	30	55	42	62	154	27
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	10	100	11	100	10	100	53	37	72	41	57	47	223	22
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	34	79	0	.	10	100	11	100	0	.	177	23	210	21	142	35	583	14
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	11	100	0	.	180	31	566	31	162	45	926	22
	dead trees	0	.	0	.	0	.	0	.	0	.	30	100	149	84	0	.	236	34	35	77	0	.	1462	15	2368	16	456	21	4736	10
	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	.	671	42	2268	28	322	71	2655	19	391	50	1207	30	7399	13	11428	10	4930	14	31271	6
	exposed wood core	0	.	0	.	0	.	0	.	0	.	183	54	176	31	8	100	422	24	41	72	52	44	1077	15	1167	15	784	20	3908	8
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	9	100	10	100	0	.	25	100	0	.	18	65	10	100	78	43	64	41	213	25
	stem breakage	0	.	0	.	0	.	0	.	0	.	4	100	28	58	0	.	14	77	10	100	0	.	103	31	149	30	179	30	486	16
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	34	52	4	100	47	43
	canker	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	153	33	72	35	233	24
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	93	43	172	32	0	.	487	20	128	56	82	49	1153	16	1420	13	359	24	3892	8
	inclusion	0	.	0	.	0	.	0	.	0	.	13	75	62	72	0	.	4	100	0	.	14	77	130	53	48	41	12	58	282	30
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	19	71	0	.	4	100	0	.	0	.	30	74	104	36	61	44	219	24
	other type of damage	0	.	0	.	0	.	0	.	0	.	10	100	33	48	0	.	85	37	0	.	10	100	83	31	194	29	136	27	550	15
	lying living trees	0	.	0	.	0	.	0	.	0	.	4	100	25	85	11	100	10	100	0	.	42	100	65	44	148	35	44	46	349	23
	dead trees	0	.	0	.	0	.	0	.	0	.	188	43	407	28	60	100	721	30	78	64	159	44	1787	18	1745	13	579	20	5724	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	728	26	0	.	367	42	0	.	0	.	2725	22	3855	16	1378	23	4289	15	1195	28	1090	27	6119	13	9750	11	6993	12	38488	5
	exposed wood core	106	32	0	.	40	58	0	.	0	.	366	25	511	20	174	37	875	18	306	30	73	36	1012	17	1223	18	982	18	5669	7
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	13	75	23	59	28	55	31	65	30	75	10	100	80	47	106	33	80	41	401	18
	stem breakage	0	.	0	.	18	71	0	.	0	.	23	59	60	58	33	53	103	41	44	72	0	.	73	41	164	40	256	26	774	15
	defoliation >50%	4	100	0	.	0	.	0	.	0	.	0	.	44	47	19	71	19	100	0	.	0	.	9	100	72	42	72	43	239	23
	canker	0	.	0	.	0	.	0	.	0	.	8	71	4	100	11	100	38	45	0	.	37	48	31	52	25	50	90	33	244	18
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	66	39	0	.	36	57	0	.	0	.	289	24	366	24	51	43	519	19	200	32	97	35	1133	17	995	15	354	23	4105	7
	inclusion	0	.	0	.	0	.	0	.	0	.	161	59	4	100	12	100	96	40	10	72	0	.	91	46	137	45	18	64	528	24
	stag-headedness	0	.	0	.	0	.	0	.	0	.	18	71	69	43	21	71	7	100	0	.	9	100	17	64	65	50	77	39	283	21
	other type of damage	15	78	0	.	0	.	0	.	0	.	52	46	73	42	30	58	110	35	28	56	24	72	95	31	148	28	217	26	792	12
	lying living trees	0	.	0	.	0	.	0	.	0	.	11	100	11	100	24	73	44	46	82	79	20	71	214	45	10	100	71	43	486	26
	dead trees	124	49	0	.	100	57	0	.	0	.	758	26	875	26	462	44	1396	20	149	37	402	35	1259	16	1188	17	968	16	7681	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	6539	11	0	.	374	47	111	100	0	.	4146	16	4619	15	3716	14	3724	18	2179	23	2760	21	5367	14	5580	15	6432	13	45547	5
	exposed wood core	997	17	0	.	23	61	10	100	0	.	710	24	758	21	625	27	548	22	429	37	456	32	1080	17	756	19	1802	16	8193	7
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	44	47	10	100	32	58	20	71	21	61	39	61	10	100	91	40	57	42	324	19
	stem breakage	31	58	0	.	0	.	10	100	0	.	21	61	133	31	51	48	43	46	20	71	10	100	93	50	138	34	217	24	766	13
	defoliation >50%	40	61	0	.	0	.	0	.	0	.	26	55	8	71	11	100	0	.	26	72	13	77	25	60	8	71	23	61	180	24
	canker	93	31	0	.	0	.	0	.	0	.	33	52	31	49	147	25	44	47	0	.	45	49	75	53	85	43	70	42	623	14
	bark necrosis	0	.	0	.	0	.	0	.	0	.	4	100	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	12	58
	resinosis	316	24	0	.	0	.	0	.	0	.	318	26	393	32	98	28	246	25	213	39	73	40	552	21	950	17	306	22	3464	8
	inclusion	88	38	0	.	0	.	0	.	0	.	168	44	67	49	91	45	31	52	0	.	0	.	95	65	41	50	41	47	623	19
	stag-headedness	72	38	0	.	0	.	0	.	0	.	47	45	58	41	34	71	30	58	90	54	0	.	64	77	31	58	194	29	621	17
	other type of damage	74	34	0	.	0	.	0	.	0	.	84	38	38	45	46	41	50	42	48	100	117	37	169	29	131	30	205	28	962	12
	lying living trees	27	86	0	.	4	100	0	.	0	.	0	.	44	51	18	71	30	58	51	60	39	61	72	45	59	62	103	47	447	20
	dead trees	686	21	0	.	18	62	0	.	0	.	969	22	1188	23	708	18	768	22	309	33	510	27	1123	18	1082	18	1495	22	8857	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15113	8	38	100	426	61	132	73	197	50	3835	18	7058	11	6376	13	2649	19	2259	23	1934	26	4001	15	4080	15	8191	12	56289	4
	exposed wood core	2267	12	10	100	33	71	0	.	59	62	709	21	1499	17	1078	22	809	24	637	26	511	29	1090	22	875	21	2055	14	11634	6
	crack, woundwood rib	36	52	0	.	0	.	0	.	0	.	18	71	16	64	38	51	36	51	14	76	4	100	24	61	16	100	115	39	316	20
	stem breakage	152	29	0	.	0	.	0	.	0	.	41	46	145	32	95	38	23	62	23	61	38	61	98	40	143	36	169	25	928	12
	defoliation >50%	36	52	0	.	0	.	0	.	0	.	10	100	4	100	35	71	10	100	0	.	4	100	20	71	29	74	81	48	229	25
	canker	160	32	0	.	9	100	0	.	8	71	28	54	104	32	125	28	28	61	10	100	26	74	45	53	52	70	103	29	697	13
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	617	16	0	.	17	100	0	.	0	.	400	28	423	19	149	30	134	38	180	34	118	39	329	27	462	24	119	30	2947	8
	inclusion	204	29	0	.	0	.	0	.	0	.	29	60	104	39	158	40	46	54	26	54	51	64	63	60	23	61	222	43	927	16
	stag-headedness	84	35	0	.	0	.	0	.	10	100	0	.	110	33	21	71	30	58	89	49	38	61	58	51	53	58	190	35	683	16
	other type of damage	142	33	0	.	4	100	0	.	0	.	40	44	96	33	52	44	96	34	77	53	28	74	62	40	139	38	189	28	924	12
	lying living trees	21	71	0	.	0	.	0	.	0	.	19	71	114	58	8	100	19	71	0	.	0	.	109	45	14	76	49	52	353	26
	dead trees	1316	13	10	100	18	80	33	89	14	76	795	20	989	18	582	20	529	29	385	26	312	32	1133	20	683	20	1362	16	8163	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11418	9	1742	24	2375	22	1941	26	1626	31	2308	21	9322	10	8104	12	2159	28	1418	30	1824	24	3447	17	2676	18	6212	13	56574	4
	exposed wood core	2988	12	258	31	209	34	201	29	295	45	283	26	1236	18	1178	19	510	28	484	37	950	24	764	23	404	23	2327	14	12087	6
	crack, woundwood rib	17	62	14	100	15	78	0	.	45	73	12	100	62	40	35	50	18	65	23	61	4	100	47	65	40	61	94	34	425	17
	stem breakage	141	32	19	71	24	72	66	72	61	100	21	71	84	32	103	50	20	100	22	54	72	46	70	43	98	32	94	53	895	15
	defoliation >50%	23	61	0	.	32	85	11	100	0	.	48	51	35	71	20	71	20	71	10	100	26	74	51	50	21	78	101	35	397	18
	canker	159	28	38	59	18	61	4	100	9	71	31	54	229	23	127	32	29	58	23	72	23	61	0	.	26	65	81	47	797	12
	bark necrosis	0	.	4	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	4	100	4	100	22	57
	resinosis	374	20	38	82	57	54	23	56	79	44	122	35	371	22	334	29	61	47	115	46	202	30	164	30	250	28	35	65	2226	9
	inclusion	226	52	22	78	23	72	13	58	0	.	0	.	146	59	94	56	48	81	14	77	17	64	41	60	18	64	83	41	745	23
	stag-headedness	241	23	25	73	10	100	21	71	0	.	10	100	107	42	43	50	10	100	0	.	29	59	93	36	20	71	391	38	999	17
	other type of damage	150	30	31	57	18	80	0	.	49	59	24	61	74	47	161	43	55	50	19	63	85	46	44	57	107	34	204	29	1020	13
	lying living trees	29	58	0	.	0	.	0	.	10	100	10	100	31	58	9	100	30	74	10	100	27	63	59	48	97	41	109	38	421	18
	dead trees	1163	14	172	32	176	29	301	40	148	38	239	33	1128	18	778	19	375	26	385	32	538	26	1057	22	540	25	1420	20	8419	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	12894	8	2755	17	6185	12	8740	11	9377	10	924	33	5560	13	4696	15	1553	27	946	33	909	34	1920	24	1809	21	5269	14	63538	4
	exposed wood core	2125	12	701	23	775	17	962	16	1124	15	98	51	1318	16	701	21	305	44	238	40	229	39	371	29	394	29	2061	18	11402	6
	crack, woundwood rib	41	44	0	.	15	74	56	52	18	64	0	.	12	100	59	45	4	100	19	71	17	64	10	100	10	100	67	38	327	18
	stem breakage	269	24	19	71	31	49	37	50	151	29	10	100	86	40	34	76	9	100	0	.	9	100	23	85	48	60	124	33	852	13
	defoliation >50%	86	34	0	.	0	.	0	.	80	77	15	77	4	100	13	58	0	.	70	100	9	100	14	77	39	78	439	39	770	26
	canker	91	31	21	55	70	40	104	43	64	36	12	100	60	46	25	85	22	85	0	.	9	100	4	100	15	79	23	61	520	15
	bark necrosis	0	.	0	.	0	.	0	.	14	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	47	73
	resinosis	209	29	64	69	297	28	428	24	339	20	9	100	156	32	48	37	8	71	53	51	14	78	74	41	43	46	11	100	1754	10
	inclusion	88	39	13	100	5	100	4	100	39	69	16	78	54	51	34	68	0	.	38	78	0	.	40	50	10	100	29	55	370	19
	stag-headedness	191	28	91	46	114	32	10	100	153	46	10	100	107	41	25	61	0	.	10	100	9	100	64	50	65	56	590	24	1440	13
	other type of damage	209	25	98	59	58	43	42	44	131	31	28	86	81	36	29	74	29	75	7	100	4	100	59	59	74	42	89	47	938	13
	lying living trees	32	52	0	.	0	.	10	100	48	52	10	100	0	.	22	71	0	.	0	.	19	100	61	53	33	67	75	62	310	24
	dead trees	1526	13	330	30	510	21	657	20	930	15	50	74	577	23	613	23	230	31	133	68	189	43	650	24	275	30	1702	18	8371	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5815	12	8807	10	5580	13	11242	8	23341	6	0	.	1437	32	1489	24	844	43	638	45	191	50	1413	29	132	65	7070	12	67999	3
	exposed wood core	1042	19	982	13	562	18	1201	11	2243	9	0	.	81	41	601	54	511	59	109	48	93	72	245	45	13	100	2743	13	10427	6
	crack, woundwood rib	4	100	4	100	4	100	17	63	94	37	0	.	0	.	35	58	30	74	11	100	0	.	0	.	0	.	77	42	276	21
	stem breakage	83	35	178	28	89	64	73	33	146	24	0	.	0	.	9	100	19	71	0	.	0	.	0	.	0	.	75	51	671	15
	defoliation >50%	0	.	40	60	10	100	66	70	120	42	0	.	10	100	4	100	19	71	10	100	0	.	21	100	30	100	583	34	914	24
	canker	82	43	57	38	12	75	100	34	156	29	0	.	0	.	12	100	19	83	17	63	0	.	0	.	14	78	76	41	545	15
	bark necrosis	0	.	22	100	0	.	9	100	25	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	48
	resinosis	114	39	149	42	86	36	604	19	1048	13	0	.	27	73	39	64	20	82	20	82	0	.	13	100	4	100	33	51	2156	9
	inclusion	21	55	37	61	46	60	8	71	32	48	0	.	0	.	35	100	70	87	0	.	0	.	0	.	0	.	116	42	364	26
	stag-headedness	130	34	144	36	108	46	43	47	129	28	0	.	22	71	35	100	10	100	0	.	0	.	19	63	0	.	776	21	1416	13
	other type of damage	77	40	193	27	102	33	95	28	233	25	0	.	10	100	79	53	30	58	4	100	0	.	31	74	8	100	260	24	1124	11
	lying living trees	15	79	13	76	22	71	14	77	63	45	0	.	10	100	12	100	0	.	10	100	0	.	0	.	0	.	65	47	225	23
	dead trees	870	17	787	18	765	19	633	16	1263	11	0	.	184	46	228	35	93	47	67	70	38	61	301	39	24	100	3138	14	8392	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52508	3	13342	8	15307	8	22166	6	34540	5	14905	9	34493	5	26169	6	18725	8	9220	11	10037	10	36997	5	48124	4	48982	3	385515	1
	exposed wood core	9524	5	1951	11	1643	11	2374	9	3722	8	2357	11	5620	8	4385	11	4043	11	2283	14	2364	14	6790	7	6450	7	13439	5	66943	2
	crack, woundwood rib	98	29	18	81	34	49	73	42	156	31	96	31	133	26	227	21	174	28	129	29	92	33	200	27	471	20	649	13	2549	7
	stem breakage	676	14	217	24	162	38	186	31	358	23	129	26	536	16	324	23	237	24	119	34	139	31	546	16	910	13	1221	11	5760	5
	defoliation >50%	189	24	40	60	41	70	77	62	200	40	100	32	106	33	102	34	87	37	125	60	53	46	170	27	247	25	1314	20	2850	11
	canker	586	14	115	28	109	30	208	27	237	22	112	28	433	16	447	16	181	24	61	40	140	27	191	27	499	19	531	14	3848	6
	bark necrosis	0	.	26	87	0	.	9	100	40	42	4	100	0	.	18	65	0	.	0	.	0	.	0	.	4	100	36	90	137	34
	resinosis	1697	9	250	33	494	20	1054	14	1466	10	1278	13	1918	10	717	16	1599	11	1013	17	590	16	4504	8	5405	6	1566	10	23551	3
	inclusion	628	22	71	43	74	44	25	41	71	44	386	32	445	25	425	24	300	29	88	40	83	44	528	22	306	24	563	21	3993	8
	stag-headedness	718	13	260	26	233	27	73	36	292	27	86	33	494	17	188	29	101	31	200	34	95	34	398	20	409	19	2337	11	5884	6
	other type of damage	667	14	322	25	182	24	137	24	413	19	238	22	438	16	398	23	465	16	195	35	267	24	720	13	1011	11	1442	10	6895	4
	lying living trees	122	31	13	76	25	62	24	61	120	32	55	42	235	32	104	32	142	28	164	45	147	38	759	18	927	21	678	18	3515	9
	dead trees	5686	6	1299	14	1587	12	1624	13	2356	9	3030	11	5496	9	3431	10	4348	10	1541	15	2147	13	8772	7	7905	7	11122	6	60343	2
	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. 1426340. Birmensdorf, Swiss Federal Research Institute WSL

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