NFI Results

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: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	.	815	49	6313	17	9514	13	2120	25	18762	9
	exposed wood core	0	.	0	.	73	63	1002	19	1177	16	440	34	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	.	0	.	143	49	544	27	1072	21	206	41	1965	14
	inclusion	0	.	0	.	23	72	62	54	37	66	40	100	162	37
	stag-headedness	0	.	0	.	19	100	37	54	25	77	32	70	113	36
	other type of damage	0	.	0	.	59	76	113	36	211	27	64	62	447	20
	lying living trees	0	.	0	.	0	.	147	49	482	50	0	.	629	40
	dead trees	0	.	0	.	379	60	1437	22	1623	20	347	31	3787	14
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	7263	20	5914	21	7184	15	4646	17	25007	9
	exposed wood core	0	.	0	.	810	22	730	24	832	27	657	28	3029	12
	crack, woundwood rib	0	.	0	.	24	77	18	100	0	.	32	64	74	44
	stem breakage	0	.	0	.	82	44	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	.	0	.	781	23	610	23	822	22	315	33	2528	12
	inclusion	0	.	0	.	53	56	106	43	38	69	14	71	211	29
	stag-headedness	0	.	0	.	29	80	18	100	74	61	151	41	271	30
	other type of damage	0	.	0	.	93	51	70	48	115	43	97	38	375	23
	lying living trees	0	.	0	.	64	65	17	100	44	61	77	57	202	34
	dead trees	0	.	0	.	1779	25	1579	32	1118	21	383	30	4859	15
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	1162	32	0	.	13426	12	5326	19	8928	15	7756	18	36599	7
	exposed wood core	157	40	0	.	2327	15	689	22	1189	26	851	25	5213	10
	crack, woundwood rib	0	.	0	.	116	42	108	58	71	61	87	52	382	26
	stem breakage	33	70	0	.	331	32	59	67	90	42	218	38	732	20
	defoliation >50%	0	.	0	.	82	52	15	100	106	49	7	100	210	33
	canker	0	.	0	.	68	45	0	.	7	100	49	62	124	35
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	62	51	0	.	1445	16	601	25	918	19	424	32	3450	10
	inclusion	0	.	0	.	315	54	104	67	101	56	32	64	552	35
	stag-headedness	0	.	0	.	190	35	15	100	79	56	53	57	337	26
	other type of damage	19	100	0	.	321	26	89	45	111	44	160	39	700	18
	lying living trees	0	.	0	.	146	37	248	64	17	100	42	72	453	38
	dead trees	171	58	0	.	4212	17	921	28	1324	24	785	22	7413	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6201	15	39	.	18157	10	5218	21	5027	19	5502	19	40144	6
	exposed wood core	664	22	0	.	3623	14	856	26	644	26	1345	23	7132	9
	crack, woundwood rib	0	.	0	.	121	42	17	100	53	74	22	100	213	33
	stem breakage	17	100	0	.	344	24	37	78	75	44	129	42	602	18
	defoliation >50%	25	75	0	.	77	50	10	100	22	77	0	.	133	35
	canker	78	44	0	.	261	24	26	77	43	61	79	56	486	19
	bark necrosis	0	.	0	.	15	72	0	.	0	.	0	.	15	72
	resinosis	119	33	0	.	892	19	337	34	552	27	260	33	2160	12
	inclusion	35	71	0	.	379	33	32	64	25	100	50	59	521	26
	stag-headedness	89	45	0	.	237	30	0	.	17	100	101	41	444	21
	other type of damage	22	58	0	.	211	28	109	48	114	54	123	49	579	20
	lying living trees	53	75	0	.	122	47	35	71	35	71	112	71	358	31
	dead trees	660	30	0	.	4515	14	723	26	1220	23	630	32	7748	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15422	10	1663	37	20715	9	2943	24	2875	28	5721	19	49340	6
	exposed wood core	2106	15	169	44	4398	12	741	32	818	30	1618	22	9851	8
	crack, woundwood rib	46	61	0	.	80	42	0	.	0	.	45	61	170	30
	stem breakage	140	47	0	.	362	23	19	100	106	62	129	36	756	18
	defoliation >50%	58	57	0	.	0	.	36	71	0	.	36	70	130	38
	canker	123	43	7	100	285	23	9	100	96	70	87	43	606	19
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	536	21	62	53	835	17	265	39	462	29	57	52	2217	11
	inclusion	206	38	51	73	233	42	7	100	27	76	131	55	655	23
	stag-headedness	75	50	0	.	325	27	74	62	62	72	142	53	678	20
	other type of damage	118	41	0	.	393	22	24	77	98	61	135	36	768	17
	lying living trees	17	100	22	100	78	52	119	64	7	100	18	100	261	35
	dead trees	1264	16	220	43	3314	12	804	31	375	34	1125	26	7103	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15783	10	7536	17	19789	9	2834	25	2978	23	5672	19	54592	6
	exposed wood core	3346	15	503	24	4292	13	550	44	412	28	1790	21	10892	8
	crack, woundwood rib	32	79	41	58	116	36	18	100	54	75	75	50	335	24
	stem breakage	248	43	71	49	309	34	18	100	111	39	40	100	796	21
	defoliation >50%	17	100	0	.	82	47	60	67	0	.	34	65	193	32
	canker	183	37	70	63	369	23	0	.	44	64	117	57	783	17
	bark necrosis	0	.	17	100	0	.	0	.	0	.	13	71	31	64
	resinosis	367	28	178	29	981	21	165	39	215	43	9	100	1916	13
	inclusion	188	62	33	81	407	42	40	71	18	100	44	62	730	29
	stag-headedness	223	31	15	100	182	39	71	61	36	71	633	42	1160	25
	other type of damage	124	37	44	61	329	28	0	.	144	37	143	36	785	16
	lying living trees	34	71	15	100	148	40	69	61	53	74	115	47	435	24
	dead trees	1296	17	463	29	3388	13	708	35	493	32	1279	24	7626	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14723	10	27892	7	11595	13	1007	51	2380	22	5524	19	63121	5
	exposed wood core	3081	14	3428	10	2243	17	250	42	743	29	2408	24	12152	7
	crack, woundwood rib	38	55	122	44	71	44	18	100	26	77	48	60	324	24
	stem breakage	258	27	209	32	171	35	35	100	51	76	145	40	869	16
	defoliation >50%	56	49	17	100	23	75	17	100	17	100	445	63	574	49
	canker	124	35	282	32	93	49	7	100	28	79	23	76	557	20
	bark necrosis	0	.	24	76	0	.	0	.	0	.	60	100	84	75
	resinosis	231	39	989	19	198	33	40	71	75	49	0	.	1534	15
	inclusion	53	55	27	76	107	57	55	57	18	100	0	.	259	31
	stag-headedness	257	30	188	31	103	65	87	60	83	74	609	34	1327	19
	other type of damage	278	27	300	24	174	44	32	71	128	44	103	69	1014	16
	lying living trees	7	100	87	52	66	60	18	100	25	77	96	85	299	35
	dead trees	1869	15	2184	14	1274	19	389	41	418	35	1699	22	7832	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	9169	12	43990	5	4877	22	668	59	111	.	7321	17	66136	4
	exposed wood core	1159	24	5226	8	1096	51	76	60	0	.	2621	18	10177	9
	crack, woundwood rib	7	100	77	51	79	60	0	.	0	.	54	74	217	34
	stem breakage	104	43	450	28	18	100	0	.	0	.	7	100	579	23
	defoliation >50%	0	.	21	58	18	100	0	.	0	.	708	49	748	46
	canker	59	48	226	30	52	65	0	.	0	.	25	77	362	23
	bark necrosis	42	100	31	64	0	.	0	.	0	.	0	.	73	63
	resinosis	128	70	1665	13	29	100	25	100	0	.	0	.	1847	13
	inclusion	44	85	174	45	110	100	0	.	0	.	126	49	454	34
	stag-headedness	199	36	492	28	0	.	7	100	0	.	899	28	1597	19
	other type of damage	111	46	558	21	102	67	0	.	0	.	192	38	963	17
	lying living trees	37	71	128	41	38	71	0	.	0	.	92	53	295	27
	dead trees	1272	20	2868	11	308	29	70	61	0	.	2566	18	7084	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	62460	4	81121	3	96638	4	30223	8	38997	6	44261	5	353699	2
	exposed wood core	10514	7	9325	6	18863	6	4894	10	5816	10	11729	7	61140	3
	crack, woundwood rib	122	35	240	29	607	18	197	38	246	32	387	23	1800	11
	stem breakage	800	19	729	20	1617	12	292	28	666	19	733	18	4838	7
	defoliation >50%	156	32	38	54	282	26	173	37	216	31	1230	36	2096	22
	canker	567	18	584	21	1127	12	49	49	443	29	427	23	3198	8
	bark necrosis	42	100	72	44	15	72	0	.	0	.	73	83	202	40
	resinosis	1443	14	2894	10	5304	8	2587	12	4116	9	1272	16	17617	4
	inclusion	526	29	284	33	1628	19	406	25	264	30	437	25	3545	11
	stag-headedness	843	16	695	22	1083	15	309	28	377	28	2620	16	5927	9
	other type of damage	672	17	902	15	1681	12	437	20	922	16	1016	15	5631	6
	lying living trees	148	38	253	30	661	19	653	30	664	37	552	26	2932	13
	dead trees	6533	8	5735	8	19170	6	6631	11	6571	9	8813	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. 1426321. Birmensdorf, Swiss Federal Research Institute WSL

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