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: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern 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	±%
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	.	815	49	0	.	6313	17	9591	13	2042	26	18762	9
	exposed wood core	0	.	73	63	0	.	1002	19	1185	16	433	35	2692	12
	crack, woundwood rib	0	.	0	.	0	.	17	100	42	61	25	77	84	43
	stem breakage	0	.	0	.	0	.	24	77	146	41	0	.	171	37
	defoliation >50%	0	.	0	.	0	.	35	100	17	100	0	.	53	75
	canker	0	.	0	.	0	.	0	.	101	72	14	71	114	64
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	143	49	0	.	544	27	1109	20	169	46	1965	14
	inclusion	0	.	23	72	0	.	62	54	37	66	40	100	162	37
	stag-headedness	0	.	19	100	0	.	37	54	25	77	32	70	113	36
	other type of damage	0	.	59	76	0	.	113	36	218	26	56	69	447	20
	lying living trees	0	.	0	.	0	.	147	49	482	50	0	.	629	40
	dead trees	0	.	379	60	0	.	1437	22	1642	20	329	33	3787	14
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	5634	23	1620	36	6472	20	6866	15	4415	18	25007	9
	exposed wood core	0	.	691	24	118	51	828	22	814	27	577	31	3029	12
	crack, woundwood rib	0	.	17	100	7	100	18	100	0	.	32	64	74	44
	stem breakage	0	.	64	48	18	100	99	45	87	67	65	59	333	27
	defoliation >50%	0	.	0	.	0	.	0	.	54	58	0	.	54	58
	canker	0	.	0	.	0	.	7	100	125	55	32	71	165	44
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	551	23	247	53	610	23	805	22	315	33	2528	12
	inclusion	0	.	36	67	17	100	114	40	38	69	6	100	211	29
	stag-headedness	0	.	29	80	0	.	55	74	74	61	114	44	271	30
	other type of damage	0	.	93	51	18	100	77	45	98	47	90	40	375	23
	lying living trees	0	.	42	85	22	100	71	62	44	61	23	76	202	34
	dead trees	0	.	1509	27	280	52	1653	31	1074	22	343	33	4859	15
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	1162	32	10013	14	3286	22	5696	18	9874	14	6567	19	36599	7
	exposed wood core	157	40	1756	17	392	31	947	25	1272	25	689	27	5213	10
	crack, woundwood rib	0	.	88	49	28	81	108	58	108	47	51	74	382	26
	stem breakage	33	70	236	37	76	75	77	57	90	42	218	38	732	20
	defoliation >50%	0	.	67	60	15	100	15	100	106	49	7	100	210	33
	canker	0	.	51	50	17	100	0	.	7	100	49	62	124	35
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	62	51	1034	19	396	30	616	25	970	18	372	35	3450	10
	inclusion	0	.	277	61	38	64	104	67	101	56	32	64	552	35
	stag-headedness	0	.	150	40	40	71	15	100	79	56	53	57	337	26
	other type of damage	19	100	236	31	85	48	89	45	118	42	153	41	700	18
	lying living trees	0	.	65	53	81	52	248	64	17	100	42	72	453	38
	dead trees	171	58	3032	20	990	41	1146	25	1411	23	662	24	7413	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6201	15	11398	13	6853	15	5678	20	4582	20	5433	19	40144	6
	exposed wood core	664	22	2258	18	1344	22	948	24	573	28	1345	23	7132	9
	crack, woundwood rib	0	.	22	100	99	47	17	100	53	74	22	100	213	33
	stem breakage	17	100	190	31	124	40	67	62	75	44	129	42	602	18
	defoliation >50%	25	75	48	54	44	74	10	100	6	100	0	.	133	35
	canker	78	44	84	43	161	31	59	52	25	77	79	56	486	19
	bark necrosis	0	.	9	100	6	100	0	.	0	.	0	.	15	72
	resinosis	119	33	636	22	242	37	352	33	552	27	260	33	2160	12
	inclusion	35	71	229	46	150	46	32	64	25	100	50	59	521	26
	stag-headedness	89	45	144	33	93	58	0	.	17	100	101	41	444	21
	other type of damage	22	58	130	35	106	51	127	44	70	61	123	49	579	20
	lying living trees	53	75	40	73	99	53	35	71	18	100	112	71	358	31
	dead trees	660	30	3101	18	1396	19	837	24	1125	24	630	32	7748	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15755	10	12353	12	9488	15	3650	22	2371	27	5721	19	49340	6
	exposed wood core	2150	15	2730	17	1930	18	604	30	818	30	1618	22	9851	8
	crack, woundwood rib	46	61	31	66	49	54	0	.	0	.	45	61	170	30
	stem breakage	140	47	190	30	155	39	37	71	106	62	129	36	756	18
	defoliation >50%	58	57	0	.	0	.	36	71	0	.	36	70	130	38
	canker	136	39	124	35	133	34	31	72	96	70	87	43	606	19
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	536	21	542	19	339	28	281	37	462	29	57	52	2217	11
	inclusion	206	38	71	57	221	44	7	100	18	100	131	55	655	23
	stag-headedness	92	44	179	34	129	46	74	62	62	72	142	53	678	20
	other type of damage	118	41	250	27	180	37	24	77	61	77	135	36	768	17
	lying living trees	17	100	100	46	0	.	119	64	7	100	18	100	261	35
	dead trees	1287	16	2280	16	1372	20	718	25	320	35	1125	26	7103	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	19989	9	12786	11	10656	13	2864	25	2691	25	5604	19	54592	6
	exposed wood core	3733	14	1839	17	2581	18	615	41	350	31	1774	21	10892	8
	crack, woundwood rib	45	60	78	45	66	48	18	100	54	75	75	50	335	24
	stem breakage	271	40	156	36	215	44	18	100	96	42	40	100	796	21
	defoliation >50%	17	100	57	58	25	80	60	67	0	.	34	65	193	32
	canker	196	35	248	28	177	36	0	.	44	64	117	57	783	17
	bark necrosis	0	.	0	.	17	100	0	.	6	100	7	100	31	64
	resinosis	492	23	382	24	653	28	165	39	215	43	9	100	1916	13
	inclusion	195	60	234	63	199	47	40	71	18	100	44	62	730	29
	stag-headedness	223	31	143	46	54	58	71	61	36	71	633	42	1160	25
	other type of damage	172	32	131	43	219	34	0	.	120	42	143	36	785	16
	lying living trees	52	58	109	47	37	72	69	61	53	74	115	47	435	24
	dead trees	1673	15	1824	18	1678	19	725	34	447	35	1279	24	7626	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	39116	6	8824	13	6401	18	1069	48	2188	23	5524	19	63121	5
	exposed wood core	5837	9	1678	19	1248	21	339	41	643	30	2408	24	12152	7
	crack, woundwood rib	152	38	6	100	81	41	18	100	18	100	48	60	324	24
	stem breakage	441	21	164	37	33	71	35	100	51	76	145	40	869	16
	defoliation >50%	72	44	7	100	15	100	17	100	17	100	445	63	574	49
	canker	364	26	113	44	23	75	7	100	28	79	23	76	557	20
	bark necrosis	24	76	0	.	0	.	0	.	0	.	60	100	84	75
	resinosis	1192	18	164	38	74	43	46	63	58	57	0	.	1534	15
	inclusion	80	44	26	78	81	71	55	57	18	100	0	.	259	31
	stag-headedness	364	24	126	57	58	52	87	60	83	74	609	34	1327	19
	other type of damage	532	19	173	41	54	69	32	71	121	46	103	69	1014	16
	lying living trees	73	56	18	100	69	59	18	100	25	77	96	85	299	35
	dead trees	3780	10	857	24	716	25	441	38	339	37	1699	22	7832	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52791	5	2060	38	2513	28	1340	40	111	100	7321	17	66136	4
	exposed wood core	6191	8	112	53	1122	50	131	47	0	.	2621	18	10177	9
	crack, woundwood rib	84	48	37	100	42	71	0	.	0	.	54	74	217	34
	stem breakage	554	24	18	100	0	.	0	.	0	.	7	100	579	23
	defoliation >50%	21	58	0	.	18	100	0	.	0	.	708	49	748	46
	canker	285	26	29	100	23	75	0	.	0	.	25	77	362	23
	bark necrosis	73	63	0	.	0	.	0	.	0	.	0	.	73	63
	resinosis	1785	13	29	100	8	100	25	100	0	.	0	.	1847	13
	inclusion	155	39	110	100	62	100	0	.	0	.	126	49	454	34
	stag-headedness	629	23	0	.	62	100	7	100	0	.	899	28	1597	19
	other type of damage	660	19	18	100	93	72	0	.	0	.	192	38	963	17
	lying living trees	165	36	0	.	38	71	0	.	0	.	92	53	295	27
	dead trees	4017	10	103	45	273	32	126	47	0	.	2566	18	7084	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	135014	2	63884	5	40817	6	33082	7	38275	6	42628	5	353699	2
	exposed wood core	18733	4	11138	7	8735	10	5415	10	5655	10	11464	7	61140	3
	crack, woundwood rib	327	24	278	27	372	21	197	38	275	30	351	24	1800	11
	stem breakage	1456	14	1020	15	621	22	358	25	651	20	733	18	4838	7
	defoliation >50%	194	28	179	33	119	40	173	37	200	33	1230	36	2096	22
	canker	1059	14	648	17	534	18	104	37	426	30	427	23	3198	8
	bark necrosis	96	51	9	100	23	79	0	.	6	100	67	90	202	40
	resinosis	4186	8	3480	9	1960	14	2638	11	4170	9	1183	17	17617	4
	inclusion	671	24	1007	27	768	23	413	24	255	30	430	26	3545	11
	stag-headedness	1397	13	789	18	436	26	346	27	377	28	2583	16	5927	9
	other type of damage	1523	12	1091	14	754	19	462	20	806	16	995	16	5631	6
	lying living trees	360	25	374	24	345	26	707	28	647	38	498	28	2932	13
	dead trees	11588	5	13085	8	6706	10	7083	11	6359	10	8631	8	53452	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. 1426319. Birmensdorf, Swiss Federal Research Institute WSL

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