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: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	exposed wood core	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	crack, woundwood rib	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	stem breakage	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	defoliation >50%	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	canker	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	inclusion	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	stag-headedness	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other type of damage	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	1369	28	7113	12	11007	9	3324	14	22813	6
	exposed wood core	0	.	0	.	215	40	1136	21	1432	13	515	18	3298	10
	crack, woundwood rib	0	.	0	.	31	63	21	100	81	37	71	35	204	24
	stem breakage	0	.	0	.	64	44	261	20	361	19	131	34	817	12
	defoliation >50%	0	.	0	.	16	74	31	49	28	55	10	100	84	31
	canker	0	.	0	.	5	100	24	47	178	31	58	44	265	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	216	37	982	29	935	16	289	25	2421	14
	inclusion	0	.	0	.	22	52	8	71	38	53	22	100	90	36
	stag-headedness	0	.	0	.	39	53	86	36	119	30	36	61	281	20
	other type of damage	0	.	0	.	29	53	102	30	199	27	84	31	414	16
	lying living trees	0	.	0	.	0	.	82	50	350	45	62	51	494	33
	dead trees	0	.	0	.	362	43	1166	17	2016	17	377	22	3920	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	6502	13	6522	14	10283	11	4829	13	28137	6
	exposed wood core	0	.	0	.	1019	18	679	19	1323	15	658	19	3679	9
	crack, woundwood rib	0	.	0	.	95	29	43	46	90	35	100	39	328	18
	stem breakage	0	.	0	.	191	26	162	35	216	28	68	37	638	16
	defoliation >50%	0	.	0	.	123	48	14	77	74	41	27	54	238	29
	canker	0	.	0	.	43	48	52	40	144	30	178	44	416	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	0	.	931	14	901	16	1608	14	490	19	3929	8
	inclusion	0	.	0	.	20	68	49	57	4	100	29	56	102	34
	stag-headedness	0	.	0	.	79	48	52	79	119	36	111	36	361	22
	other type of damage	0	.	0	.	101	34	135	27	107	26	113	27	456	14
	lying living trees	0	.	0	.	157	71	21	100	94	49	10	100	282	44
	dead trees	0	.	0	.	1564	20	1338	23	1606	14	502	20	5009	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	1129	23	0	.	13140	8	5391	14	8016	11	7292	12	34968	5
	exposed wood core	79	34	0	.	2446	11	644	17	1303	19	993	18	5464	8
	crack, woundwood rib	37	44	0	.	133	28	111	60	110	45	76	38	467	21
	stem breakage	28	58	0	.	475	19	142	37	154	26	235	25	1035	12
	defoliation >50%	0	.	0	.	154	31	22	55	82	69	27	50	285	27
	canker	43	41	0	.	190	25	36	48	78	49	99	30	447	16
	bark necrosis	4	100	0	.	26	100	0	.	12	75	0	.	42	66
	resinosis	120	33	0	.	1838	12	680	19	1363	16	527	18	4528	8
	inclusion	11	100	0	.	284	37	24	85	75	40	23	61	417	27
	stag-headedness	0	.	0	.	96	34	40	50	66	50	113	33	315	20
	other type of damage	4	100	0	.	281	43	22	54	84	52	127	29	518	26
	lying living trees	0	.	0	.	222	42	146	59	0	.	52	55	420	31
	dead trees	142	41	0	.	4052	12	981	22	1103	19	730	17	7008	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6576	12	23	.	19269	7	4748	15	5653	14	6852	12	43122	5
	exposed wood core	943	16	0	.	3742	10	797	22	1020	18	1829	15	8331	6
	crack, woundwood rib	99	34	0	.	167	31	39	50	44	46	73	38	421	17
	stem breakage	155	28	0	.	631	16	128	36	203	24	194	30	1311	11
	defoliation >50%	11	100	0	.	152	27	34	67	36	48	44	51	277	20
	canker	231	25	0	.	480	16	63	49	142	28	97	35	1013	11
	bark necrosis	8	71	0	.	29	58	0	.	10	100	11	100	59	40
	resinosis	470	20	0	.	1845	14	280	28	806	16	250	24	3651	9
	inclusion	35	43	0	.	461	30	47	52	18	82	124	32	685	22
	stag-headedness	55	42	0	.	197	22	66	53	68	38	178	30	565	15
	other type of damage	102	34	0	.	239	20	53	52	88	32	111	33	593	14
	lying living trees	4	100	0	.	231	35	66	49	13	100	124	38	438	23
	dead trees	403	20	0	.	4115	10	681	20	1238	18	1171	24	7608	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15829	7	1533	24	23080	7	3334	17	3672	18	8341	12	55789	4
	exposed wood core	2027	12	288	32	5397	9	793	25	859	19	2348	12	11712	6
	crack, woundwood rib	412	18	0	.	245	21	20	71	37	48	115	32	829	12
	stem breakage	208	27	34	52	676	15	56	46	188	31	236	21	1399	10
	defoliation >50%	106	42	20	71	168	28	29	74	65	55	67	37	456	18
	canker	192	23	38	60	658	15	29	53	137	41	183	30	1236	11
	bark necrosis	33	49	0	.	59	54	0	.	0	.	76	43	168	29
	resinosis	678	23	219	44	1239	12	245	29	678	25	193	32	3253	9
	inclusion	107	28	28	62	493	23	10	100	64	52	136	36	838	16
	stag-headedness	126	31	9	100	181	26	52	45	91	46	161	37	620	16
	other type of damage	151	28	9	100	250	20	8	100	30	44	228	24	675	13
	lying living trees	21	70	11	100	107	37	79	58	20	71	86	69	324	27
	dead trees	1130	14	145	37	2858	10	721	28	640	24	1278	18	6773	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14960	8	6435	13	21713	7	3808	18	3137	17	6488	13	56541	4
	exposed wood core	3591	11	819	21	5393	10	375	29	569	23	2096	16	12843	6
	crack, woundwood rib	212	21	68	37	148	26	10	100	10	100	25	62	472	14
	stem breakage	196	24	225	35	466	18	14	77	90	45	87	38	1078	12
	defoliation >50%	35	66	11	100	87	42	14	77	0	.	53	49	199	26
	canker	263	22	61	38	607	21	15	100	69	38	67	52	1082	14
	bark necrosis	92	56	0	.	45	65	0	.	0	.	114	58	251	35
	resinosis	396	21	254	22	1112	16	162	39	298	27	77	46	2299	10
	inclusion	277	42	53	51	497	38	0	.	21	61	62	58	910	25
	stag-headedness	263	23	20	71	214	27	61	53	60	53	347	33	966	15
	other type of damage	175	27	34	45	239	27	19	71	31	49	153	45	651	17
	lying living trees	40	61	35	52	76	41	9	100	43	50	59	61	261	23
	dead trees	1049	14	496	21	2614	11	641	24	461	26	1035	22	6295	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	17270	7	27682	6	12200	10	1382	26	2274	21	5632	13	66441	4
	exposed wood core	2913	10	3545	8	2943	19	293	33	488	30	2021	17	12204	6
	crack, woundwood rib	491	20	207	20	125	31	0	.	32	49	47	46	901	13
	stem breakage	276	24	601	15	218	25	44	56	97	45	157	30	1393	10
	defoliation >50%	19	71	33	52	53	48	68	55	0	.	241	36	415	24
	canker	194	24	300	19	153	31	4	100	13	77	44	40	708	13
	bark necrosis	12	58	77	34	38	61	0	.	0	.	123	60	249	33
	resinosis	281	23	1085	13	231	26	67	51	97	41	0	.	1761	10
	inclusion	92	35	78	37	101	50	10	100	4	100	58	46	343	21
	stag-headedness	334	21	237	22	158	41	24	61	60	71	290	30	1103	13
	other type of damage	217	26	348	17	220	25	26	60	54	47	144	27	1011	11
	lying living trees	0	.	37	48	35	64	39	61	14	76	0	.	126	31
	dead trees	1732	12	2207	12	1169	16	363	31	299	26	1385	18	7155	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	9297	9	48743	4	4790	16	491	44	371	45	7864	11	71557	3
	exposed wood core	1559	15	5857	6	1034	31	141	58	88	74	2123	13	10801	6
	crack, woundwood rib	239	34	479	14	74	42	6	100	0	.	45	51	843	13
	stem breakage	114	28	822	13	63	47	0	.	0	.	157	39	1156	11
	defoliation >50%	50	45	215	29	21	71	13	100	0	.	398	32	698	21
	canker	93	30	550	16	69	79	17	100	0	.	55	42	785	14
	bark necrosis	56	70	68	30	0	.	0	.	0	.	74	43	198	27
	resinosis	316	26	2829	9	42	55	29	74	0	.	22	100	3238	9
	inclusion	87	47	206	31	53	83	25	84	0	.	87	42	458	21
	stag-headedness	155	31	496	17	50	43	0	.	0	.	596	21	1298	12
	other type of damage	49	41	567	14	42	47	0	.	0	.	172	28	830	12
	lying living trees	15	79	144	31	14	77	0	.	0	.	68	48	242	24
	dead trees	812	17	3085	9	545	26	135	52	20	100	2241	16	6838	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	65062	3	84416	3	102063	3	32790	5	44413	5	50622	3	379366	1
	exposed wood core	11113	5	10509	5	22190	5	4857	9	7080	7	12582	5	68331	2
	crack, woundwood rib	1489	10	754	11	1018	11	250	31	404	18	550	14	4465	5
	stem breakage	978	11	1683	10	2784	7	807	14	1309	10	1266	10	8826	4
	defoliation >50%	220	26	280	24	775	14	225	25	285	27	866	18	2652	9
	canker	1017	10	950	12	2204	9	240	21	761	14	780	15	5952	5
	bark necrosis	205	33	145	23	197	29	0	.	22	61	398	27	967	15
	resinosis	2262	10	4387	7	7455	6	3347	11	5784	7	1848	9	25082	3
	inclusion	608	22	365	21	1932	15	172	29	224	24	539	17	3841	9
	stag-headedness	933	12	762	13	1016	12	381	20	583	16	1832	11	5508	6
	other type of damage	698	13	959	10	1402	12	365	16	594	14	1132	11	5149	5
	lying living trees	80	39	227	23	844	21	440	26	534	31	462	22	2587	11
	dead trees	5268	6	5933	7	17280	5	6025	9	7383	7	8717	7	50607	3
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1426316. Birmensdorf, Swiss Federal Research Institute WSL

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