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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NFI5
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 2018/26
		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	4859	16	132	100	501	56	13272	12	1333	32	4388	21	444	56	0	.	3008	27	2374	23	5234	20	3062	21	115	100	1333	46	2117	25	1459	29	2631	30	698	48	4743	18	344	61	180	100	11526	13	1189	49	277	63	6258	14	71477	4
	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	169	42	0	.	68	100	317	27	31	63	394	34	25	100	0	.	109	46	140	42	72	43	117	46	12	100	0	.	62	52	14	100	66	69	39	71	277	30	20	100	110	100	575	25	32	100	13	100	174	38	2837	11
	indeterminable	809	24	7	100	121	73	1535	18	286	38	596	23	25	100	0	.	741	34	295	30	576	28	416	30	6	100	229	47	317	31	212	37	253	39	75	70	859	26	67	50	0	.	1364	18	110	46	65	66	754	17	9716	6
	wood harvest	232	33	0	.	0	.	1333	20	83	48	162	44	7	100	0	.	0	.	71	61	294	41	161	34	0	.	52	100	74	51	69	68	57	53	6	100	333	35	0	.	0	.	426	30	0	.	7	100	638	24	4006	10
	other human activity	54	49	0	.	39	100	81	39	0	.	66	59	15	100	0	.	72	48	14	100	92	91	33	71	0	.	0	.	44	60	0	.	31	71	0	.	43	62	0	.	0	.	168	38	18	100	0	.	44	62	815	18
	weather	46	74	0	.	0	.	83	36	59	52	72	41	0	.	0	.	0	0	67	60	99	52	14	70	15	100	15	100	50	58	17	100	13	71	0	.	101	42	6	100	0	.	115	55	36	71	0	.	108	46	915	15
	lying living trees	34	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	78	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	36	71	20	100	0	.	15	100	0	.	0	.	10	100	155	33
	dead trees	435	31	0	.	82	100	794	19	125	45	642	35	95	52	34	100	356	37	397	45	295	28	296	35	0	.	146	49	78	41	59	67	134	57	111	55	369	41	128	100	147	100	1150	20	245	59	51	76	500	23	6670	8
	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	3513	19	687	48	1426	40	13046	10	1357	32	3673	20	0	.	191	74	6134	16	2531	25	6143	19	4259	20	641	59	1892	37	3103	25	793	36	2846	26	2305	26	1317	31	4646	20	400	59	6694	17	3282	27	445	62	4322	19	75647	5
	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	.	59	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	75	0	.	0	.	0	.	0	.	0	.	69	59	0	.	6	100	0	.	0	.	0	.	191	44
	various	164	53	58	56	33	79	420	22	44	86	193	63	0	.	0	.	325	28	76	50	221	36	106	52	294	86	195	66	299	43	17	100	110	55	64	51	89	49	569	45	248	62	333	34	76	46	45	61	270	43	4249	12
	indeterminable	507	24	167	63	324	41	1699	14	198	39	719	33	0	.	56	78	2113	18	439	29	932	22	635	25	51	59	415	36	464	25	199	41	375	32	350	42	274	31	923	24	201	65	1172	19	645	24	17	100	854	22	13729	6
	wood harvest	152	52	65	80	13	71	922	18	50	62	43	53	0	.	7	100	37	62	136	42	175	35	88	52	0	.	19	100	117	47	167	41	215	45	66	65	37	61	90	100	0	.	345	32	36	71	8	100	311	37	3099	10
	other human activity	139	38	24	73	40	61	285	44	9	100	93	77	0	.	0	.	154	45	35	71	64	82	112	44	44	100	0	.	74	62	0	.	0	.	7	100	164	96	130	45	15	100	136	63	56	58	0	.	17	100	1598	17
	weather	64	49	0	.	58	52	206	36	74	55	123	40	0	.	0	.	106	43	58	52	195	41	134	65	0	.	17	100	60	61	0	.	66	53	81	53	87	51	96	50	0	0	211	37	49	54	0	.	172	44	1857	12
	lying living trees	17	100	0	.	18	100	41	61	35	100	15	100	0	.	0	.	35	71	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	7	100	0	.	79	70	0	.	0	.	18	100	0	.	17	100	300	29
	dead trees	439	28	110	73	220	42	1427	17	176	56	673	32	0	.	18	100	1225	25	614	39	1431	30	469	29	45	100	275	37	297	31	172	56	168	44	561	58	210	53	804	32	50	63	1054	23	680	41	59	76	448	36	11626	8
	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	1068	39	305	73	461	44	10703	13	1581	36	2033	29	0	.	610	35	13425	11	2660	23	2271	25	1194	27	354	81	1180	34	5021	20	1290	39	2346	26	1760	28	350	52	10467	17	1003	49	4708	17	11549	13	367	46	1288	37	77993	5
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	67	0	.	0	.	0	.	0	.	0	.	124	64
	rockfall	7	100	0	.	0	.	183	57	7	100	20	76	0	.	17	100	282	61	35	100	0	.	24	77	0	.	33	71	21	100	94	100	38	84	25	100	0	.	315	40	0	.	288	47	153	51	0	.	0	.	1543	20
	various	17	100	7	100	7	100	508	34	35	61	248	47	0	.	105	67	627	21	216	56	132	34	0	.	8	100	21	77	322	30	59	52	29	78	206	58	0	0	783	24	7	100	211	41	442	23	84	69	20	58	4096	10
	indeterminable	208	49	6	100	75	60	2430	14	218	47	412	30	0	.	272	45	3800	12	528	31	488	33	126	54	66	73	370	40	1026	24	201	52	451	29	340	33	92	53	3561	15	110	48	1038	20	2806	15	67	65	202	36	18895	5
	wood harvest	82	52	14	100	18	100	330	30	34	70	84	65	0	.	171	51	296	28	113	37	242	44	41	56	50	100	0	.	45	75	0	.	169	52	165	47	97	73	40	61	29	79	122	48	7	100	36	100	73	65	2258	12
	other human activity	0	.	0	.	24	100	207	45	0	.	108	68	0	.	0	.	182	33	76	63	54	57	33	80	0	.	41	71	117	41	0	.	40	64	98	94	0	.	77	55	71	100	42	85	270	47	17	100	0	.	1458	17
	weather	66	74	0	.	0	.	282	33	125	47	74	64	0	.	19	100	513	25	58	72	44	62	0	.	0	.	15	100	211	31	31	63	174	39	12	100	0	.	961	33	0	.	180	39	572	35	0	.	63	62	3398	13
	lying living trees	24	76	0	.	25	100	15	100	0	.	0	.	0	.	19	100	73	79	0	.	0	.	0	.	0	.	0	.	95	52	41	72	34	71	7	100	0	.	231	52	0	.	40	71	217	55	0	.	17	100	837	24
	dead trees	193	39	82	93	313	49	2338	17	441	35	581	33	0	.	419	54	2542	15	690	31	590	37	36	71	12	100	255	59	1391	23	236	40	522	28	492	32	0	.	3388	19	168	60	1301	22	2876	19	183	45	395	40	19444	6
	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	101	72	136	100	7	100	8178	14	646	43	728	44	0	.	1555	36	13041	12	1073	35	1379	31	560	49	1105	70	1684	39	2632	28	0	.	694	44	1304	36	0	.	8194	15	2411	28	3455	26	9602	14	0	.	425	43	58911	5
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	275	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	95	0	.	0	.	0	.	0	.	0	.	408	74
	rockfall	0	.	0	.	18	100	1030	25	229	85	199	93	0	.	243	42	675	22	61	71	0	0	177	64	0	.	579	51	273	42	0	.	122	58	124	43	0	.	731	28	568	47	193	46	906	28	0	.	52	100	6180	11
	various	0	.	0	.	82	100	189	28	0	.	6	100	0	.	43	54	875	22	59	60	132	81	8	100	54	74	55	65	118	46	0	.	0	0	36	66	0	.	1271	28	97	53	95	40	486	34	0	.	17	100	3622	13
	indeterminable	14	100	25	100	68	100	2047	15	132	50	244	52	0	.	440	38	4831	13	266	52	489	40	0	.	119	60	348	37	761	26	0	.	383	48	295	35	0	.	2934	17	568	29	637	27	2779	15	0	.	203	44	17582	6
	wood harvest	0	.	0	.	0	.	249	39	0	.	0	.	0	.	91	55	125	39	0	.	90	83	27	100	0	.	71	100	46	63	0	.	7	100	0	.	0	.	7	100	37	100	31	64	109	48	0	.	14	100	904	19
	other human activity	0	.	0	.	0	.	152	41	0	.	98	100	0	.	89	72	183	41	8	100	17	100	0	.	25	100	0	.	70	84	0	.	0	.	14	100	0	.	96	51	0	.	8	100	136	59	0	.	0	.	895	21
	weather	14	100	0	.	0	.	308	27	0	.	6	100	0	.	58	53	702	24	14	71	148	57	0	.	0	.	15	100	56	65	0	.	25	75	57	78	0	.	474	24	77	46	40	61	360	24	0	.	17	100	2371	11
	lying living trees	7	100	0	.	0	.	84	58	0	.	0	.	0	.	58	69	276	56	0	.	55	75	0	.	0	.	0	.	15	100	0	.	19	100	0	.	0	.	202	52	0	.	0	.	201	48	0	.	0	.	917	24
	dead trees	42	100	25	100	50	100	2661	18	222	57	410	54	0	.	543	52	4648	15	402	39	785	29	136	63	163	63	278	41	702	26	0	.	306	52	331	55	0	.	2231	18	421	35	667	33	2744	17	0	.	395	80	18160	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	41	100	118	72	249	71	2039	28	34	100	549	65	0	.	328	57	2962	24	98	100	132	100	49	100	324	59	311	66	823	39	0	.	254	55	685	44	0	.	6122	19	1279	43	591	45	3202	21	0	.	0	.	20190	9
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	100	0	.	0	.	0	.	0	.	0	.	104	94
	rockfall	0	.	27	100	0	.	573	51	147	100	29	73	0	.	411	50	734	32	38	100	0	.	17	100	518	59	152	70	494	41	0	.	474	48	22	75	0	.	1078	21	188	67	295	60	463	29	0	.	0	.	5662	13
	various	0	.	31	81	143	78	13	100	0	.	0	.	0	.	30	65	33	52	0	.	0	.	0	.	79	91	24	77	123	68	0	.	0	.	6	100	0	.	909	47	33	63	10	100	161	46	0	.	0	.	1595	29
	indeterminable	7	100	67	78	21	100	704	28	24	100	293	70	0	.	205	54	1062	27	31	100	93	100	14	100	131	62	56	66	449	39	0	.	56	68	239	46	0	.	2111	20	303	49	91	52	1246	26	0	.	0	.	7203	10
	wood harvest	0	.	0	.	0	.	39	61	0	.	15	100	0	.	23	75	18	100	0	.	7	100	0	.	0	.	0	.	25	100	0	.	14	100	61	100	0	.	7	100	0	.	18	100	31	64	0	.	0	.	259	32
	other human activity	0	.	0	.	0	.	26	77	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	12	100	0	.	80	68	18	100	0	.	47	86	0	.	0	.	208	37
	weather	0	.	0	.	35	100	74	56	17	100	63	72	0	.	49	55	237	45	0	.	0	.	0	.	36	100	87	66	35	71	0	.	17	100	0	.	0	.	848	40	100	79	62	65	175	43	0	.	0	.	1834	21
	lying living trees	17	100	6	100	43	100	18	100	0	.	0	.	0	.	18	100	53	74	0	.	0	.	0	.	18	100	0	.	7	100	0	.	0	.	0	.	0	.	149	43	0	.	0	.	50	55	0	.	0	.	379	26
	dead trees	17	100	37	84	71	79	737	41	34	100	287	58	0	.	282	50	1240	26	7	100	68	100	21	100	68	65	88	66	325	47	0	.	278	55	183	58	0	.	2188	23	262	43	278	63	1480	23	0	.	0	.	7949	11
	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	.	61	100	0	.	1245	34	0	.	240	94	0	.	22	100	1183	31	0	.	0	.	151	100	105	77	319	100	637	65	0	.	0	.	19	100	0	.	1777	29	394	69	266	97	813	33	0	.	0	.	7231	15
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	33	100
	rockfall	0	.	67	100	0	.	514	35	0	.	73	83	0	.	46	100	496	43	0	.	0	.	0	.	135	76	44	100	383	79	0	.	0	.	29	100	0	.	501	35	70	100	95	77	584	46	0	.	0	.	3038	18
	various	0	.	12	100	0	.	103	65	0	.	0	.	0	.	12	100	25	77	0	.	0	.	0	.	21	100	0	.	7	100	0	.	0	.	17	100	0	.	49	64	0	.	0	.	29	61	0	.	0	.	276	31
	indeterminable	0	.	12	100	0	.	469	41	0	.	21	100	0	.	22	100	486	34	0	.	0	.	18	100	105	71	160	100	132	58	0	.	0	.	54	74	0	.	1105	34	89	81	45	74	416	42	0	.	0	.	3134	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	.	19	100	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	49	61
	weather	0	.	0	.	0	.	68	59	0	.	0	.	0	.	15	100	77	47	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	183	34	0	.	0	.	74	50	0	.	0	.	460	22
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	71	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	58	100	35	100	0	.	104	71	0	.	0	.	307	41
	dead trees	0	.	45	100	0	.	452	39	0	.	29	100	0	.	34	100	481	35	0	.	0	.	36	100	133	60	275	100	201	64	0	.	0	.	50	100	0	.	619	34	256	61	137	71	553	39	0	.	0	.	3300	16
	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	9583	11	1439	31	2644	26	48483	6	4951	17	11611	12	444	56	2705	24	39753	6	8736	12	15160	11	9275	12	2643	35	6717	18	14332	11	3543	20	8772	14	6770	15	6410	15	31551	7	5668	19	27240	8	29637	7	1088	34	12293	10	311449	2
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	288	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	380	47	0	.	0	.	0	.	0	.	0	.	669	49
	rockfall	7	100	94	77	18	100	2360	19	384	64	322	61	0	.	717	33	2188	18	134	50	0	0	219	53	652	49	864	37	1171	33	94	100	635	37	199	34	0	.	2695	13	827	36	877	28	2106	19	0	.	52	100	16614	7
	various	350	32	108	40	334	47	1552	15	110	43	840	26	25	100	189	41	1994	13	491	29	557	26	231	33	468	57	295	46	931	20	90	42	205	38	370	35	365	26	3600	17	495	40	1224	17	1226	17	142	46	482	28	16675	6
	indeterminable	1545	16	284	43	610	29	8884	7	859	21	2284	17	25	100	995	24	13033	7	1559	17	2578	14	1209	17	478	30	1579	20	3149	12	611	25	1518	18	1353	18	1225	20	10702	8	1271	21	4346	10	8001	9	148	43	2014	12	70258	2
	wood harvest	465	25	80	68	31	65	2873	12	168	33	304	31	7	100	292	35	476	21	320	25	809	23	318	25	50	100	143	63	307	27	236	35	461	29	298	36	467	29	145	65	66	66	942	19	183	34	51	73	1036	19	10526	5
	other human activity	194	31	24	73	104	50	751	23	9	100	365	40	15	100	89	72	611	20	140	40	227	46	177	34	69	73	41	71	344	28	0	.	71	48	132	72	207	77	391	26	104	72	355	32	527	30	17	100	61	53	5022	9
	weather	191	36	0	.	93	50	1021	15	274	29	338	26	0	.	142	34	1635	15	197	33	485	26	148	59	51	76	150	44	454	22	48	54	294	27	150	42	188	33	2568	18	177	49	607	21	1266	18	0	.	360	28	10836	6
	lying living trees	98	40	6	100	86	61	158	38	35	100	15	100	0	.	94	50	474	37	25	78	55	75	0	.	18	100	92	82	131	41	41	72	54	58	14	71	36	71	740	25	35	100	55	58	589	29	0	.	44	59	2895	12
	dead trees	1126	18	299	42	736	28	8409	9	998	23	2620	17	95	52	1330	29	10492	9	2110	19	3169	17	994	19	421	34	1317	27	2993	14	467	30	1408	20	1727	25	579	32	9357	9	1304	23	4587	11	8577	10	293	35	1739	23	67148	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. 1267465. Birmensdorf, Swiss Federal Research Institute WSL

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