Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
total number of stems with two most important forms of damages
slope (in 20% classes) · type of damage to trees (13 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
slope (in 20% 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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
to 20%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14267	10	3563	19	8604	15	15026	11	16149	8	2290	31	5392	21	2520	24	635	63	500	54	127	71	1189	49	3064	27	819	45	74146	4
	exposed wood core	2057	13	632	22	1177	17	1697	16	2349	11	246	39	599	28	351	40	75	90	44	61	0	.	79	59	698	35	161	46	10165	6
	crack, woundwood rib	21	58	17	100	56	76	23	77	44	50	0	0	0	.	7	100	7	100	0	.	0	.	0	.	18	100	0	.	194	30
	stem breakage	64	45	60	51	57	50	124	35	314	26	24	73	140	43	43	74	0	.	0	.	0	.	36	71	35	71	6	100	903	15
	defoliation >50%	297	34	108	49	444	31	104	35	268	28	21	100	74	67	82	79	47	74	136	73	0	.	0	.	7	100	20	100	1608	15
	canker	80	43	28	61	106	45	145	34	204	35	20	76	78	40	48	86	27	76	18	100	0	.	32	100	49	74	0	.	834	16
	bark necrosis	49	63	7	100	0	.	0	.	24	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	98	42
	resinosis	651	18	131	38	475	33	1029	25	1389	19	190	38	413	37	163	37	65	67	55	76	7	100	32	80	206	41	0	.	4805	10
	inclusion	72	44	7	100	62	53	14	71	17	100	9	100	8	100	0	.	0	.	15	71	7	100	0	.	7	100	0	.	218	25
	stag-headedness	58	52	44	60	82	66	46	54	147	39	21	100	88	41	93	100	0	.	92	100	0	.	17	100	0	.	0	.	689	24
	other type of damage	247	28	59	48	159	32	137	31	221	27	84	63	98	48	0	0	0	.	17	100	0	.	7	100	81	54	67	78	1178	13
	lying living trees	25	78	17	100	0	.	0	.	63	51	0	.	0	.	14	100	15	100	0	.	0	.	0	.	0	.	20	100	155	33
	dead trees	1358	19	325	39	1116	22	833	19	1156	17	345	50	455	26	327	43	75	60	201	76	48	77	245	59	356	37	270	60	7111	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
21-40 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	16136	9	3341	20	2690	25	6037	15	8839	13	4578	23	11711	13	7785	15	2374	24	1115	36	393	47	3847	24	6987	15	6664	16	82498	4
	exposed wood core	2923	12	542	25	390	33	1100	17	1525	18	407	25	1736	16	1421	18	495	35	467	45	99	58	515	39	1254	20	1168	23	14042	6
	crack, woundwood rib	47	50	0	.	17	100	34	71	42	60	6	100	0	.	6	100	0	0	18	100	0	.	25	76	99	45	7	100	301	23
	stem breakage	435	24	119	39	7	100	7	100	208	35	203	57	197	33	125	43	40	61	0	.	0	.	111	71	98	40	119	49	1670	13
	defoliation >50%	337	31	121	59	133	83	75	45	373	34	72	51	393	65	213	42	80	63	96	56	0	.	39	100	67	60	198	79	2197	18
	canker	268	31	17	100	20	58	99	51	60	45	39	59	233	30	130	39	26	100	87	82	0	.	67	54	113	49	277	36	1434	14
	bark necrosis	0	0	41	85	0	.	0	.	121	71	0	0	55	58	18	100	0	.	0	.	0	.	0	.	0	.	7	100	241	41
	resinosis	767	20	234	37	78	52	246	31	512	25	735	38	527	23	513	23	125	35	394	49	54	52	508	27	1550	20	246	36	6488	9
	inclusion	71	48	45	72	17	100	0	.	79	84	112	71	95	67	36	54	7	100	15	100	7	100	25	76	48	47	91	60	648	23
	stag-headedness	277	26	52	75	0	.	50	55	83	43	0	.	223	41	73	58	43	72	0	.	0	.	0	0	44	62	168	37	1013	15
	other type of damage	238	27	70	44	95	53	30	63	244	27	57	59	216	40	161	36	55	58	52	74	6	100	76	50	154	38	156	40	1611	12
	lying living trees	0	.	35	100	0	.	0	.	33	71	30	71	37	71	25	77	8	100	0	.	0	.	18	100	52	58	103	57	340	26
	dead trees	2122	16	475	31	284	34	641	26	947	23	886	29	1985	23	1238	29	342	33	165	46	72	61	807	36	1402	23	901	28	12268	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
41-60 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	9783	12	3888	22	917	39	1828	30	1977	27	3931	23	7206	15	6800	16	5520	17	1657	36	1846	27	13172	12	14372	11	14566	13	87463	4
	exposed wood core	2168	14	562	30	219	43	290	41	537	29	708	24	1160	21	1343	18	1379	22	141	40	695	29	2207	17	2494	15	3868	14	17770	6
	crack, woundwood rib	52	58	7	100	0	.	0	0	0	.	0	.	0	.	35	68	16	100	0	.	6	100	43	61	122	38	114	39	394	20
	stem breakage	227	33	145	41	25	100	0	.	151	37	28	63	141	40	194	47	131	49	18	100	53	58	283	34	184	37	427	29	2008	12
	defoliation >50%	291	45	59	52	17	100	0	.	31	64	112	65	186	62	145	49	86	48	0	.	84	82	149	55	258	36	611	28	2028	15
	canker	125	39	52	53	7	100	20	100	7	100	25	60	106	34	178	34	103	41	23	75	14	71	208	38	203	36	461	21	1531	12
	bark necrosis	14	71	0	.	0	0	0	0	0	.	0	.	0	.	16	71	17	100	0	.	6	100	0	.	0	.	67	67	120	42
	resinosis	343	28	127	50	33	66	51	67	20	58	555	34	763	22	536	28	1019	25	212	42	223	39	2226	16	2830	13	1474	19	10413	7
	inclusion	135	59	0	.	0	.	7	100	0	.	121	62	48	71	179	57	107	45	10	100	0	.	269	48	149	39	99	49	1125	19
	stag-headedness	241	31	153	41	83	66	0	.	0	.	62	51	183	37	22	75	60	51	0	.	15	100	95	53	200	37	856	32	1972	16
	other type of damage	243	29	38	61	25	76	18	72	32	78	89	53	91	55	113	40	188	32	72	61	82	55	214	28	279	28	749	22	2234	11
	lying living trees	17	100	66	53	0	.	0	.	33	71	22	100	15	100	75	53	52	74	0	.	71	61	253	49	126	52	372	42	1101	20
	dead trees	2442	16	818	24	317	47	123	41	512	33	911	25	1410	25	2108	18	1474	22	228	47	728	34	3373	17	2520	16	4332	15	21294	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
61-80 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	4764	20	695	41	0	.	619	37	302	56	2002	29	6208	19	2215	27	5947	17	3344	23	3010	27	10376	13	11037	12	12823	11	63341	5
	exposed wood core	1649	21	167	55	0	.	165	43	171	58	485	47	1507	21	833	28	1295	22	1194	30	916	24	2635	15	2368	17	4063	14	17449	6
	crack, woundwood rib	17	71	0	.	0	.	0	.	7	100	0	.	6	100	21	100	0	.	51	76	12	100	102	40	35	71	213	36	462	22
	stem breakage	58	67	0	.	0	.	23	77	7	100	0	.	134	59	90	48	158	36	86	53	92	63	264	32	263	37	234	31	1409	14
	defoliation >50%	28	50	0	.	0	.	19	100	0	.	49	64	30	62	62	53	65	54	43	74	55	50	262	65	113	44	720	35	1445	22
	canker	29	50	0	.	0	.	0	.	17	100	67	58	218	50	28	63	129	48	145	49	25	76	158	35	208	33	642	24	1668	14
	bark necrosis	24	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	25	76	0	.	0	.	55	100	63	52	175	41
	resinosis	343	44	0	.	0	.	0	.	41	72	325	41	794	26	134	37	872	21	437	30	360	42	1699	17	3587	16	931	22	9524	8
	inclusion	8	100	0	.	0	.	17	100	0	.	233	55	69	43	84	72	65	55	20	58	60	90	140	38	98	39	116	38	910	20
	stag-headedness	114	48	0	.	0	.	54	76	0	.	37	72	130	43	60	58	75	44	46	72	22	75	338	34	92	54	569	33	1538	16
	other type of damage	65	47	14	100	0	.	0	0	17	100	29	72	156	37	136	41	136	48	102	47	48	55	242	32	326	31	767	23	2039	12
	lying living trees	19	100	7	100	0	.	0	.	0	.	0	.	121	51	0	.	18	100	212	67	142	57	236	42	311	51	289	39	1355	21
	dead trees	1237	22	212	58	0	.	171	60	76	63	737	37	1978	20	1006	38	1854	20	678	31	1025	33	2986	16	3736	17	3719	14	19415	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
81-100 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	648	47	156	73	0	.	0	.	41	100	661	56	1014	32	1251	31	2244	27	1763	34	886	38	3532	19	3264	22	7583	16	23046	8
	exposed wood core	723	49	330	60	0	.	0	.	7	100	327	57	1107	37	569	32	891	32	412	43	924	32	1198	20	1348	26	3343	15	11181	9
	crack, woundwood rib	0	.	17	100	0	.	0	.	0	.	0	.	0	.	6	100	17	100	0	.	6	100	17	100	23	78	36	64	123	35
	stem breakage	0	.	0	.	0	.	0	.	0	.	0	.	36	71	113	64	57	70	18	100	18	100	68	52	68	60	118	38	495	23
	defoliation >50%	7	100	17	100	0	.	0	.	0	.	0	.	76	74	127	70	23	78	90	85	44	62	75	73	25	77	746	42	1230	28
	canker	0	.	0	.	0	.	0	.	0	.	0	.	24	77	63	69	13	100	61	53	30	65	76	67	18	74	197	33	483	21
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	resinosis	14	70	0	.	0	.	0	.	0	.	243	49	167	47	231	39	431	44	464	44	201	44	935	37	896	27	841	28	4424	13
	inclusion	0	.	0	0	0	.	0	.	0	.	7	100	26	77	30	72	7	100	0	.	33	63	47	49	25	78	117	49	293	26
	stag-headedness	0	.	0	.	0	.	0	.	0	.	18	100	24	77	44	60	81	45	28	79	0	0	18	100	43	62	494	34	750	24
	other type of damage	0	0	17	100	0	.	0	.	0	.	26	100	113	52	92	84	90	54	55	62	40	61	192	32	110	44	795	52	1531	29
	lying living trees	0	.	0	.	0	.	0	.	17	100	0	.	36	71	56	78	0	.	0	.	18	100	68	48	88	60	200	37	482	23
	dead trees	198	51	177	71	0	.	0	.	17	100	312	54	298	36	473	38	915	38	350	37	407	39	1666	21	1095	23	2752	20	8660	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>100%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	283	71	0	.	0	.	0	.	0	.	506	68	906	48	311	77	631	45	452	61	742	51	882	31	886	32	2603	22	8202	13
	exposed wood core	105	85	0	.	0	.	0	.	0	.	207	52	599	40	451	77	358	42	244	50	613	39	731	38	558	40	1716	24	5581	14
	crack, woundwood rib	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	18	100	49	63	79	48
	stem breakage	0	0	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	68	62	36	71	105	49	25	74	262	30
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	0	17	100	0	.	22	100	18	100	104	48	183	35
	canker	0	.	0	.	0	.	0	.	0	.	0	.	48	100	6	100	55	84	0	.	42	71	14	71	0	.	42	72	207	39
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	0	21	100	0	.	0	.	0	.	0	.	0	.	8	100	30	77
	resinosis	75	100	0	.	0	.	0	.	0	.	24	73	251	52	33	100	141	60	27	73	123	50	213	42	363	48	86	61	1338	21
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	57	88	10	100	10	100	0	.	26	100	72	53	36	71	108	62	318	31
	stag-headedness	26	100	0	.	0	.	0	.	0	.	0	.	18	100	42	100	0	.	17	100	71	79	67	78	0	.	261	57	502	35
	other type of damage	11	100	0	.	0	.	0	.	0	.	50	72	7	100	33	73	36	71	0	.	53	74	37	66	36	71	280	37	544	24
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	92	73	0	.	104	71	18	100	78	78	365	38
	dead trees	115	76	0	.	0	.	0	.	0	.	165	61	484	60	159	58	297	50	294	55	367	43	595	37	312	42	821	29	3609	15
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	45881	4	11645	11	12211	12	23510	8	27309	6	13968	12	32437	7	20883	8	17351	9	8830	14	7005	15	32999	7	39610	6	45057	5	338696	2
	exposed wood core	9625	7	2233	15	1786	14	3251	11	4588	9	2381	16	6707	10	4968	11	4494	12	2501	19	3247	15	7366	9	8720	8	14320	6	76188	2
	crack, woundwood rib	148	29	41	61	73	63	57	52	93	37	6	100	6	100	75	45	40	61	68	62	24	61	187	29	316	24	418	23	1553	11
	stem breakage	785	17	324	25	88	43	154	31	680	18	255	46	676	20	565	24	387	25	122	43	232	34	798	20	753	19	929	17	6747	6
	defoliation >50%	960	20	304	31	594	30	198	26	672	22	254	35	780	37	630	26	301	28	383	37	183	43	545	37	488	24	2398	19	8690	8
	canker	501	20	97	38	133	37	263	28	288	27	151	33	708	20	453	22	353	27	334	32	110	38	556	21	590	20	1619	13	6158	6
	bark necrosis	87	46	48	74	0	0	0	0	144	61	0	0	62	53	54	54	17	100	25	76	25	79	0	.	74	79	146	39	682	21
	resinosis	2194	12	492	24	586	28	1326	20	1963	15	2072	18	2916	12	1609	14	2653	14	1589	20	967	21	5613	10	9432	8	3579	12	36992	3
	inclusion	285	32	52	63	79	47	37	55	95	71	483	35	303	31	340	36	196	32	60	40	133	48	553	26	364	22	530	23	3511	10
	stag-headedness	717	17	249	31	166	47	150	37	230	29	139	36	666	20	335	36	260	25	184	55	108	56	535	26	379	25	2347	16	6463	8
	other type of damage	804	15	198	26	280	26	186	26	514	18	335	27	681	20	535	23	506	21	300	28	229	30	768	16	986	16	2815	17	9137	7
	lying living trees	62	53	125	42	0	.	0	.	147	34	52	59	281	36	170	37	93	49	304	52	231	40	678	26	594	31	1060	21	3798	11
	dead trees	7472	8	2007	16	1717	17	1769	14	2708	12	3356	15	6610	11	5310	13	4958	13	1915	18	2647	18	9672	9	9421	9	12795	7	72357	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. 1375603. Birmensdorf, Swiss Federal Research Institute WSL

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