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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

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: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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	.	1226	31	205	52	7383	11	11066	9	2932	14	22813	6
	exposed wood core	0	.	146	36	69	100	1157	20	1494	13	431	18	3298	10
	crack, woundwood rib	0	.	20	82	11	100	21	100	85	36	67	36	204	24
	stem breakage	0	.	53	49	21	71	261	20	352	20	131	34	817	12
	defoliation >50%	0	.	16	74	0	.	31	49	28	55	10	100	84	31
	canker	0	.	5	100	0	.	38	41	178	31	44	53	265	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	204	38	11	100	982	29	970	16	253	27	2421	14
	inclusion	0	.	18	60	4	100	8	71	38	53	22	100	90	36
	stag-headedness	0	.	28	63	21	71	86	36	113	30	32	67	281	20
	other type of damage	0	.	29	53	0	.	110	29	203	26	73	34	414	16
	lying living trees	0	.	0	.	0	.	82	50	350	45	62	51	494	33
	dead trees	0	.	362	43	0	.	1174	17	2035	17	349	23	3920	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	4814	16	1551	27	7246	14	10213	11	4313	14	28137	6
	exposed wood core	0	.	824	22	145	34	778	18	1332	15	600	20	3679	9
	crack, woundwood rib	0	.	63	35	32	53	84	45	84	36	65	36	328	18
	stem breakage	0	.	177	27	14	78	162	35	216	28	68	37	638	16
	defoliation >50%	0	.	112	52	11	100	14	77	74	41	27	54	238	29
	canker	0	.	33	54	9	100	52	40	144	30	178	44	416	23
	bark necrosis	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	resinosis	0	.	677	16	199	30	967	16	1629	14	459	20	3929	8
	inclusion	0	.	20	68	0	.	49	57	4	100	29	56	102	34
	stag-headedness	0	.	75	50	4	100	62	68	123	35	97	40	361	22
	other type of damage	0	.	79	39	28	63	152	25	107	26	91	30	456	14
	lying living trees	0	.	147	76	11	100	21	100	94	49	10	100	282	44
	dead trees	0	.	1313	22	276	36	1386	22	1565	14	470	21	5009	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	1129	23	9636	10	3142	17	5919	13	8491	11	6651	12	34968	5
	exposed wood core	79	34	1897	13	383	22	856	16	1325	18	925	19	5464	8
	crack, woundwood rib	37	44	129	28	4	100	111	60	121	42	66	41	467	21
	stem breakage	28	58	370	21	95	40	162	34	152	25	227	26	1035	12
	defoliation >50%	0	.	147	32	0	.	30	49	82	69	27	50	285	27
	canker	43	41	156	27	34	67	36	48	89	45	89	31	447	16
	bark necrosis	4	100	26	100	0	.	0	.	12	75	0	.	42	66
	resinosis	120	33	1538	14	227	28	766	17	1431	15	446	19	4528	8
	inclusion	11	100	262	39	10	100	36	66	75	40	23	61	417	27
	stag-headedness	0	.	85	36	11	100	40	50	76	46	103	35	315	20
	other type of damage	4	100	263	46	18	65	22	54	104	45	107	30	518	26
	lying living trees	0	.	198	47	14	76	156	55	10	100	42	63	420	31
	dead trees	142	41	2814	14	1153	26	1182	20	1077	17	641	18	7008	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6674	11	11679	10	7735	11	5256	14	5129	15	6649	12	43122	5
	exposed wood core	943	16	2242	12	1547	18	877	20	898	18	1825	15	8331	6
	crack, woundwood rib	99	34	89	37	78	51	39	50	44	46	73	38	421	17
	stem breakage	175	27	442	20	202	27	128	36	169	26	194	30	1311	11
	defoliation >50%	11	100	136	29	16	73	34	67	36	48	44	51	277	20
	canker	231	25	280	21	192	25	71	45	142	28	97	35	1013	11
	bark necrosis	8	71	21	71	18	71	0	.	0	.	11	100	59	40
	resinosis	474	20	1454	16	379	26	325	26	769	17	250	24	3651	9
	inclusion	35	43	321	39	140	44	47	52	37	65	105	34	685	22
	stag-headedness	55	42	114	28	73	38	76	48	68	38	178	30	565	15
	other type of damage	102	34	138	27	91	33	63	47	88	32	111	33	593	14
	lying living trees	4	100	54	42	170	45	86	44	0	.	124	38	438	23
	dead trees	403	20	2643	14	1529	14	797	18	1074	19	1161	24	7608	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	16116	7	13337	9	10465	11	4229	15	3547	17	8095	12	55789	4
	exposed wood core	2065	12	3100	11	2357	13	1017	23	926	19	2247	12	11712	6
	crack, woundwood rib	412	18	157	27	88	34	20	71	63	41	89	36	829	12
	stem breakage	208	27	438	18	244	27	96	42	181	31	232	22	1399	10
	defoliation >50%	116	39	179	27	0	.	29	74	65	55	67	37	456	18
	canker	214	21	419	19	223	26	61	43	137	41	183	30	1236	11
	bark necrosis	33	49	50	61	9	100	0	.	0	.	76	43	168	29
	resinosis	690	22	952	15	416	24	324	25	696	24	175	34	3253	9
	inclusion	112	27	249	34	240	32	37	60	64	52	136	36	838	16
	stag-headedness	136	30	119	34	71	36	41	50	91	46	161	37	620	16
	other type of damage	151	28	163	26	96	31	8	100	30	44	228	24	675	13
	lying living trees	21	70	75	42	43	60	136	54	20	71	29	58	324	27
	dead trees	1130	14	1974	13	1025	16	758	24	625	24	1262	18	6773	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	19544	7	12447	9	11334	10	4094	17	2672	19	6450	13	56541	4
	exposed wood core	4253	10	2424	13	3015	14	571	26	498	26	2083	16	12843	6
	crack, woundwood rib	268	18	96	33	64	39	10	100	10	100	25	62	472	14
	stem breakage	275	21	293	22	297	29	36	68	90	45	87	38	1078	12
	defoliation >50%	45	56	57	56	30	56	14	77	0	.	53	49	199	26
	canker	351	20	480	25	110	34	15	100	60	41	67	52	1082	14
	bark necrosis	92	56	19	71	26	100	0	.	0	.	114	58	251	35
	resinosis	558	17	660	21	524	24	182	36	298	27	77	46	2299	10
	inclusion	307	39	307	57	212	35	0	.	21	61	62	58	910	25
	stag-headedness	273	22	128	37	106	33	61	53	51	60	347	33	966	15
	other type of damage	226	24	79	33	133	41	29	58	31	49	153	45	651	17
	lying living trees	60	47	76	41	15	77	9	100	43	50	59	61	261	23
	dead trees	1403	12	1385	14	1359	15	662	24	451	27	1035	22	6295	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	41288	5	8425	11	7314	12	1778	23	2003	22	5632	13	66441	4
	exposed wood core	5871	7	1555	17	1924	27	451	30	382	28	2021	17	12204	6
	crack, woundwood rib	668	16	116	32	43	41	0	.	28	55	47	46	901	13
	stem breakage	791	13	195	27	109	36	44	56	97	45	157	30	1393	10
	defoliation >50%	52	42	49	52	4	100	68	55	0	.	241	36	415	24
	canker	445	16	121	30	72	52	13	76	13	77	44	40	708	13
	bark necrosis	89	31	0	.	38	61	0	.	0	.	123	60	249	33
	resinosis	1297	12	215	27	96	31	67	51	87	44	0	.	1761	10
	inclusion	163	26	89	56	18	62	16	73	0	.	58	46	343	21
	stag-headedness	539	16	118	52	72	38	24	61	60	71	290	30	1103	13
	other type of damage	505	15	199	27	82	38	26	60	54	47	144	27	1011	11
	lying living trees	37	48	21	100	8	71	46	54	14	76	0	.	126	31
	dead trees	3520	9	793	20	784	22	426	28	247	27	1385	18	7155	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	56798	3	2357	24	3077	19	1163	30	298	51	7864	11	71557	3
	exposed wood core	7364	6	203	52	681	39	414	46	16	100	2123	13	10801	6
	crack, woundwood rib	698	15	23	73	71	47	6	100	0	.	45	51	843	13
	stem breakage	931	12	41	62	27	71	0	.	0	.	157	39	1156	11
	defoliation >50%	251	26	21	71	15	100	13	100	0	.	398	32	698	21
	canker	644	14	69	79	0	.	17	100	0	.	55	42	785	14
	bark necrosis	119	37	0	.	5	100	0	.	0	.	74	43	198	27
	resinosis	3130	9	28	73	29	54	29	74	0	.	22	100	3238	9
	inclusion	277	26	44	100	20	81	30	72	0	.	87	42	458	21
	stag-headedness	632	15	25	61	44	62	0	.	0	.	596	21	1298	12
	other type of damage	609	13	0	.	39	51	10	100	0	.	172	28	830	12
	lying living trees	164	28	0	.	10	100	0	.	0	.	68	48	242	24
	dead trees	3731	8	178	44	368	31	300	37	20	100	2241	16	6838	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	141548	2	63921	4	44823	5	37069	5	43419	5	48586	3	379366	1
	exposed wood core	20575	3	12390	5	10121	8	6120	8	6870	7	12255	5	68331	2
	crack, woundwood rib	2182	8	693	13	391	18	291	29	434	17	476	14	4465	5
	stem breakage	2409	7	2010	8	1007	13	889	13	1257	11	1254	11	8826	4
	defoliation >50%	474	18	717	15	75	37	233	24	285	27	866	18	2652	9
	canker	1928	8	1563	11	640	15	304	19	762	14	755	15	5952	5
	bark necrosis	345	22	116	38	96	40	0	.	12	75	398	27	967	15
	resinosis	6269	6	5726	7	1881	11	3643	10	5879	7	1682	10	25082	3
	inclusion	905	16	1310	20	645	19	222	25	239	23	520	17	3841	9
	stag-headedness	1636	9	692	15	403	17	391	20	582	16	1804	11	5508	6
	other type of damage	1596	8	949	16	487	17	420	15	617	14	1080	11	5149	5
	lying living trees	286	21	570	27	271	31	534	24	531	31	394	21	2587	11
	dead trees	10329	4	11463	6	6495	8	6684	8	7094	7	8543	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. 1426314. Birmensdorf, Swiss Federal Research Institute WSL

back