Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
total number of stems with two most important forms of damages
slope (in 20% classes) · cause of damage to trees (10 classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
slope (in 20% classes)	cause of damage to trees (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	various	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	weather	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
to 20%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	7098	18	104	100	595	56	14227	11	1195	33	4908	19	908	46	52	100	3093	25	3095	25	5368	19	3598	21	112	100	962	47	2765	22	2789	33	2066	28	810	50	7629	17	335	64	228	100	11979	13	1112	44	506	58	6892	14	82426	4
	fire	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	17	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	46	53
	rockfall	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100
	various	134	39	0	.	25	100	419	33	14	70	116	36	0	.	0	.	178	47	200	53	186	36	104	44	0	.	37	72	36	61	87	66	66	78	0	.	91	40	36	71	18	100	289	29	49	74	0	.	318	32	2403	11
	indeterminable	483	27	14	100	43	61	1599	16	202	40	770	23	17	100	0	.	673	38	402	37	424	27	247	42	0	.	118	62	396	32	253	37	295	48	54	100	797	28	156	50	92	100	1563	20	182	45	22	76	1173	16	9976	7
	wood harvest	487	28	7	100	0	.	674	23	77	60	223	38	0	.	0	.	17	100	105	56	358	33	181	40	0	.	0	.	160	39	14	71	104	43	0	.	213	43	0	.	0	.	311	40	0	.	0	.	530	27	3462	10
	other human activity	72	55	0	.	0	.	283	36	59	89	255	55	8	100	0	.	87	49	49	59	100	51	110	47	0	.	0	.	105	84	7	100	37	61	0	.	0	.	27	100	0	.	117	46	8	100	0	.	76	57	1399	17
	weather	59	55	0	.	0	.	279	38	7	100	174	38	0	.	0	.	19	100	91	54	109	40	7	100	0	.	44	63	52	71	0	.	66	50	22	100	109	51	14	100	0	.	183	29	0	.	0	.	81	45	1317	13
	lying living trees	20	100	0	.	0	.	0	.	0	.	23	77	0	.	0	.	17	100	0	.	17	100	0	.	0	.	15	100	17	100	17	100	0	.	0	.	31	100	0	.	0	.	8	100	0	.	17	100	34	100	218	31
	dead trees	445	31	0	.	43	100	680	22	168	42	460	32	228	62	0	.	322	55	289	36	161	36	278	37	0	.	172	75	215	51	122	65	236	54	18	100	384	41	116	87	37	100	946	24	140	58	0	.	317	31	5776	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
21-40 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	3277	19	1121	45	2084	34	14457	11	1208	32	4040	22	0	.	250	71	7275	16	2963	24	6447	17	4268	19	631	72	1980	34	3464	27	1415	34	2292	26	3060	25	1705	32	4479	23	610	53	7912	17	3595	24	492	59	4613	20	83640	4
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	27	100
	rockfall	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	54	74	0	.	0	.	36	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	182	44
	various	115	36	51	73	35	100	426	22	64	100	22	58	0	.	7	100	269	55	46	49	519	44	70	52	32	100	120	73	19	71	24	100	141	41	99	42	7	100	137	44	7	100	403	35	32	64	7	100	32	63	2684	13
	indeterminable	344	32	31	65	281	38	1910	16	293	39	712	26	0	.	0	.	1377	20	110	38	742	23	486	31	21	100	301	42	536	31	144	37	441	31	168	36	146	37	712	23	83	51	1017	22	493	29	0	.	518	23	10865	6
	wood harvest	265	31	27	62	10	100	628	27	14	100	203	43	0	.	0	.	198	42	198	37	256	37	375	44	30	100	35	83	13	71	56	66	182	42	101	65	48	74	31	100	0	.	144	29	54	75	29	100	227	29	3124	11
	other human activity	152	48	7	100	29	79	186	29	92	100	97	58	0	.	0	.	64	46	31	64	71	56	63	48	107	72	75	51	55	58	14	71	0	.	17	100	184	100	111	47	0	.	375	50	66	48	0	.	116	60	1913	17
	weather	130	34	89	61	22	75	508	22	8	100	120	40	0	.	0	.	134	39	62	48	249	32	90	45	54	72	17	100	23	76	7	100	158	39	69	50	35	81	220	38	35	100	191	36	35	71	0	.	33	45	2289	10
	lying living trees	7	100	0	.	0	.	25	78	0	.	15	100	0	.	0	.	35	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	30	100	0	.	17	100	0	.	0	.	0	.	145	39
	dead trees	432	29	33	71	181	52	1074	20	102	41	718	37	0	.	18	100	992	22	268	54	1001	42	39	70	45	100	141	59	378	33	83	47	90	48	622	68	36	71	658	30	35	100	567	25	388	46	59	76	305	34	8267	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
41-60 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	895	36	316	72	478	55	11196	13	1698	32	1932	30	0	.	1959	44	13830	11	2860	25	2791	25	1232	30	409	88	1507	36	6339	19	1044	41	2748	23	2253	27	584	48	11229	14	790	57	5025	18	12368	13	442	49	1582	34	85506	4
	fire	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	374	59	0	.	0	.	7	100	0	.	0	.	411	54
	rockfall	0	.	0	.	0	.	55	48	24	100	93	63	0	.	0	.	192	48	94	73	0	.	21	100	6	100	27	100	37	80	101	100	152	50	25	77	0	.	116	61	0	.	302	36	102	40	0	.	0	.	1347	17
	various	0	.	53	87	32	61	428	22	25	100	62	47	0	.	81	87	470	21	155	48	119	44	7	100	0	.	34	82	571	70	45	64	72	55	28	63	48	75	733	28	0	.	156	35	236	32	0	.	13	71	3370	15
	indeterminable	485	40	46	65	108	50	2167	16	333	37	361	40	0	.	415	35	2683	13	255	37	450	32	87	72	75	71	206	51	566	26	247	51	406	33	456	34	70	59	3231	15	206	62	747	23	2023	16	74	64	434	32	16132	6
	wood harvest	69	61	25	77	0	.	484	38	27	100	73	56	0	.	7	100	249	27	333	35	169	46	77	50	21	100	9	100	84	40	17	100	150	61	140	46	75	60	27	78	37	100	260	33	37	60	18	100	48	75	2435	12
	other human activity	17	100	0	.	38	71	666	44	0	.	150	46	0	.	0	.	280	35	243	62	380	52	19	100	0	.	40	71	114	69	41	100	101	62	110	85	0	.	71	50	38	100	59	57	260	56	0	.	23	77	2652	17
	weather	83	54	39	62	106	67	470	22	7	100	95	51	0	.	0	.	344	24	177	39	132	59	0	.	12	100	39	100	72	47	0	.	83	50	79	65	0	.	515	25	0	.	40	61	273	33	0	.	33	71	2601	10
	lying living trees	7	100	0	.	0	.	24	77	0	.	36	100	0	.	88	100	86	47	0	.	0	.	19	100	0	.	0	.	22	100	0	.	0	.	0	.	0	.	101	64	0	.	0	.	164	89	0	.	41	61	588	33
	dead trees	101	57	82	93	185	52	1956	19	87	61	399	34	0	.	178	63	1863	17	324	34	471	38	0	.	45	100	93	45	954	29	49	58	260	38	357	36	24	77	1662	20	62	59	861	23	1959	24	44	72	286	42	12304	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
61-80 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	108	78	150	100	121	100	8881	14	561	49	922	51	0	.	1775	32	14923	11	1442	35	1772	29	521	57	880	77	1612	39	3366	28	0	.	1003	40	1295	34	0	.	9441	14	2663	27	2507	27	10159	14	0	.	1024	44	65126	5
	fire	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	278	68	7	100	0	.	0	.	0	.	0	.	302	63
	rockfall	0	.	0	.	0	.	1083	28	222	94	156	72	0	.	523	70	998	29	7	100	58	90	177	83	21	100	501	51	215	42	0	.	186	55	68	46	0	.	364	39	535	39	116	49	812	30	0	.	110	100	6152	13
	various	0	.	0	.	0	.	221	33	7	100	15	72	0	.	69	61	572	26	62	79	217	40	8	100	0	.	164	68	56	52	0	.	56	68	66	53	0	.	408	41	94	57	139	39	296	29	0	.	41	85	2489	12
	indeterminable	0	.	18	100	25	100	2048	15	219	62	45	74	0	.	1185	45	4100	14	140	44	492	37	82	69	75	69	278	50	811	27	0	.	242	46	307	39	0	.	2254	18	360	34	571	31	2211	16	0	.	121	44	15584	7
	wood harvest	0	.	0	.	0	.	64	59	7	100	0	.	0	.	119	77	171	35	17	100	15	100	54	58	93	100	0	.	25	79	0	.	24	100	6	100	0	.	0	.	0	.	110	62	43	100	0	.	0	.	748	24
	other human activity	21	100	0	.	0	.	87	56	0	.	158	81	0	.	46	100	353	28	8	100	79	59	17	100	0	.	75	71	48	78	0	.	10	100	85	100	0	.	76	71	49	58	23	58	60	68	0	.	52	100	1246	18
	weather	0	.	0	.	18	100	467	27	24	77	76	50	0	.	130	67	528	22	49	63	148	49	0	.	44	100	58	68	44	74	0	.	139	52	61	53	0	.	298	38	149	48	69	50	485	30	0	.	0	.	2788	11
	lying living trees	0	.	0	.	0	.	72	71	0	.	0	.	0	.	117	76	208	68	0	.	36	72	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	18	100	0	.	0	.	226	42	0	.	0	.	697	29
	dead trees	14	100	7	100	46	100	1881	21	160	57	553	57	0	.	514	34	3038	16	197	43	636	33	7	100	112	58	273	43	718	29	0	.	196	51	295	53	0	.	1585	19	431	43	369	44	1905	22	0	.	187	42	13122	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
81-100 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	24	100	131	71	250	71	1992	27	41	100	473	77	0	.	345	52	3515	22	115	100	129	100	28	100	710	53	221	66	1039	34	0	.	386	51	701	47	0	.	7421	17	1385	44	761	50	4058	21	0	.	0	.	23725	9
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	84	78	0	.	0	.	0	.	0	.	0	.	102	67
	rockfall	0	.	0	.	64	100	712	49	126	100	128	100	0	.	485	52	602	35	7	100	118	100	21	100	129	87	136	100	461	47	0	.	547	50	21	74	0	.	561	32	118	60	168	62	471	32	0	.	0	.	4874	14
	various	0	.	29	100	14	100	54	50	0	.	12	100	0	.	18	74	122	40	0	.	14	100	0	.	0	.	17	100	99	58	0	.	7	100	0	.	0	.	354	32	33	81	26	75	148	36	0	.	0	.	948	16
	indeterminable	24	100	25	100	32	100	737	29	0	.	165	100	0	.	168	57	914	23	38	100	36	100	0	.	54	74	0	.	157	39	0	.	59	77	155	42	0	.	1834	19	242	47	121	47	641	26	0	.	0	.	5400	10
	wood harvest	0	.	0	.	0	.	64	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	69	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	176	53
	other human activity	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	28	71	0	.	0	.	0	.	0	.	35	71	56	100	0	.	7	100	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	172	44
	weather	17	100	13	71	18	100	90	58	17	100	123	64	0	.	40	71	185	45	0	.	0	.	0	.	18	100	21	100	7	100	0	.	17	100	57	79	0	.	395	34	63	58	52	100	130	44	0	.	0	.	1261	17
	lying living trees	0	.	0	.	0	.	17	100	0	.	0	.	0	.	18	100	35	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	28	74	0	.	0	.	105	39
	dead trees	21	100	22	76	71	100	814	46	24	100	348	57	0	.	221	58	949	28	0	.	61	100	0	.	25	100	25	77	284	46	0	.	127	64	164	61	0	.	1527	24	198	50	307	69	1333	22	0	.	0	.	6519	12
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>100%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	106	100	0	.	1013	37	0	.	224	85	0	.	28	100	1370	31	0	.	0	.	169	100	96	86	152	100	764	60	0	.	0	.	27	100	0	.	1913	29	360	62	353	83	1111	34	0	.	0	.	7684	14
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	81	0	.	0	.	0	.	0	.	0	.	102	81
	rockfall	0	.	33	100	0	.	552	44	0	.	48	72	0	.	31	100	511	43	0	.	0	.	0	.	249	78	188	100	457	81	0	.	0	.	114	71	0	.	394	44	105	75	73	91	417	41	0	.	0	.	3171	20
	various	0	.	12	100	0	.	18	74	0	.	21	100	0	.	0	.	22	74	0	.	0	.	18	100	0	.	0	.	15	100	0	.	0	.	0	.	0	.	83	58	8	100	0	.	33	81	0	.	0	.	230	30
	indeterminable	0	.	0	.	0	.	617	41	0	.	21	100	0	.	6	100	543	41	0	.	0	.	36	100	39	71	36	100	97	70	0	.	0	.	20	100	0	.	765	31	120	65	61	100	214	43	0	.	0	.	2575	17
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	17	100
	weather	0	.	6	100	0	.	42	62	0	.	21	100	0	.	15	100	115	65	0	.	0	.	0	.	39	71	0	.	55	100	0	.	0	.	0	.	0	.	84	81	0	.	13	100	22	58	0	.	0	.	412	30
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	84	71	51	75	0	.	69	79	0	.	0	.	277	42
	dead trees	0	.	61	100	0	.	444	45	0	.	71	100	0	.	37	100	391	35	0	.	0	.	44	100	59	58	239	100	37	100	0	.	0	.	9	100	0	.	420	41	123	71	137	81	419	37	0	.	0	.	2491	18
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11403	13	1928	31	3528	24	51766	6	4703	17	12499	12	908	46	4409	24	44006	6	10475	12	16507	10	9815	12	2838	34	6433	18	17738	11	5248	21	8495	13	8147	14	9918	14	34818	6	6035	19	28537	8	32403	7	1441	32	14112	10	348109	2
	fire	0	.	0	.	0	.	31	71	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	7	100	17	100	0	.	0	.	27	100	838	37	7	100	8	100	7	100	0	.	0	.	991	32
	rockfall	0	.	33	100	64	100	2454	21	372	66	471	40	0	.	1039	43	2357	18	108	64	176	73	255	59	405	55	852	41	1170	38	101	100	885	34	228	39	0	.	1474	20	757	31	660	26	1802	18	0	.	110	100	15773	8
	various	249	27	144	46	106	46	1565	13	109	63	249	24	0	.	176	47	1634	15	463	30	1055	24	206	30	32	100	372	40	797	51	156	44	341	28	194	29	145	35	1753	17	161	39	1012	18	794	17	7	100	405	27	12124	6
	indeterminable	1336	19	134	36	489	26	9078	8	1046	21	2075	16	17	100	1774	32	10291	8	946	20	2143	14	938	21	264	34	940	24	2561	13	644	25	1442	18	1161	19	1013	23	8952	8	1103	22	4080	11	5764	9	96	52	2245	12	60532	3
	wood harvest	821	20	60	45	10	100	1913	16	125	45	499	26	0	.	127	73	635	19	652	23	798	21	686	27	180	59	114	67	282	26	87	48	461	28	254	36	335	32	58	65	37	100	826	21	134	47	47	73	805	20	9946	6
	other human activity	263	33	7	100	66	52	1228	26	151	70	661	32	8	100	46	100	811	18	331	47	631	34	209	31	107	72	225	33	378	37	62	69	155	44	212	60	184	100	325	29	88	55	574	35	411	38	0	.	267	37	7399	9
	weather	290	25	147	41	164	47	1855	12	63	45	609	21	0	.	185	50	1326	14	379	25	638	22	97	42	166	41	179	38	254	33	7	100	464	24	288	30	143	43	1525	15	247	35	548	20	945	19	0	.	148	31	10668	5
	lying living trees	33	66	0	.	0	.	139	43	0	.	74	58	0	.	223	57	382	41	0	.	60	53	19	100	0	.	88	84	38	71	17	100	20	100	16	71	31	100	233	41	51	75	25	76	487	38	17	100	75	57	2030	16
	dead trees	1012	19	204	49	527	31	6849	11	540	25	2549	19	228	62	968	25	7555	9	1078	21	2329	22	368	31	287	35	944	33	2585	16	254	36	909	23	1465	33	444	36	5968	10	884	27	3188	14	6144	12	103	53	1096	18	48478	4
	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. 1426388. Birmensdorf, Swiss Federal Research Institute WSL

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