Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total number of stems with two most important forms of damages
altitude (in 200 m classes) · cause of damage to trees (10 classes)
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitude (in 200 m 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	±%
n/a	n/a	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	.
	fire	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	.
	various	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	.
	wood harvest	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	.
	weather	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	.
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	296	82	375	60	89	100	851	39	193	75	121	71	7331	11	12669	9	3885	14	25810	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	8	71	16	50
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	18	100	22	100	0	.	177	28	180	33	177	31	580	17
	various	0	.	0	.	0	.	0	.	0	.	7	100	26	83	0	.	10	100	11	100	0	.	230	25	386	22	80	38	750	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	46	71	28	63	10	100	190	34	121	78	14	76	1654	13	2221	11	665	19	4948	7
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	22	56	0	.	34	42
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	6	100	0	.	0	.	27	43	58	39	36	68	135	27
	weather	0	.	0	.	0	.	0	.	0	.	9	100	23	73	10	100	24	72	34	100	10	100	415	18	536	20	373	24	1434	11
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	11	100	0	.	180	31	566	31	162	45	926	22
	dead trees	0	.	0	.	0	.	0	.	0	.	30	100	149	84	0	.	236	34	35	77	0	.	1462	15	2368	16	456	21	4736	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1601-1800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	0	.	0	.	0	.	0	.	0	.	671	42	2268	28	322	71	2655	19	391	50	1207	30	7399	13	11428	10	4930	14	31271	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	4	100	8	71
	rockfall	0	.	0	.	0	.	0	.	0	.	8	71	56	47	0	.	83	42	19	100	0	.	219	36	221	34	254	39	858	18
	various	0	.	0	.	0	.	0	.	0	.	88	100	57	48	0	.	70	43	0	.	0	.	234	28	277	22	136	29	861	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	162	43	256	29	8	100	610	19	165	47	126	44	1361	15	2111	11	681	20	5481	7
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	64	67	104	53	8	71	187	38
	other human activity	0	.	0	.	0	.	0	.	0	.	13	75	43	78	0	.	22	61	0	.	35	89	143	49	133	53	4	100	393	28
	weather	0	.	0	.	0	.	0	.	0	.	18	72	65	39	0	.	149	44	0	.	14	78	315	23	279	27	441	23	1282	13
	lying living trees	0	.	0	.	0	.	0	.	0	.	4	100	25	85	11	100	10	100	0	.	42	100	65	44	148	35	44	46	349	23
	dead trees	0	.	0	.	0	.	0	.	0	.	188	43	407	28	60	100	721	30	78	64	159	44	1787	18	1745	13	579	20	5724	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	728	26	0	.	367	42	0	.	0	.	2725	22	3855	16	1378	23	4289	15	1195	28	1090	27	6119	13	9750	11	6993	12	38488	5
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	4	100	15	76
	rockfall	0	.	0	.	0	.	0	.	0	.	56	75	36	52	0	.	216	41	11	74	10	100	242	44	288	34	244	37	1103	18
	various	50	46	0	.	16	81	0	.	0	.	74	42	32	40	26	72	150	33	12	75	61	49	173	33	234	31	222	22	1051	12
	indeterminable	89	35	0	.	56	49	0	.	0	.	434	23	632	19	189	33	869	17	344	31	142	31	1497	14	1682	14	1040	16	6973	6
	wood harvest	4	100	0	.	0	.	0	.	0	.	47	54	85	56	4	100	54	48	14	78	4	100	34	63	107	38	4	100	356	21
	other human activity	0	.	0	.	0	.	0	.	0	.	164	58	18	66	32	58	136	36	101	68	0	.	83	50	153	41	20	83	707	21
	weather	15	100	0	.	18	71	0	.	0	.	142	34	244	31	89	39	237	29	98	55	34	52	324	23	283	27	498	22	1981	10
	lying living trees	0	.	0	.	0	.	0	.	0	.	11	100	11	100	24	73	44	46	82	79	20	71	214	45	10	100	71	43	486	26
	dead trees	124	49	0	.	100	57	0	.	0	.	758	26	875	26	462	44	1396	20	149	37	402	35	1259	16	1188	17	968	16	7681	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6539	11	0	.	374	47	111	100	0	.	4146	16	4619	15	3716	14	3724	18	2179	23	2760	21	5367	14	5580	15	6432	13	45547	5
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	10	100	4	100	0	.	0	.	23	61
	rockfall	25	63	0	.	0	.	0	.	0	.	278	44	287	45	71	63	216	39	183	46	205	56	336	29	166	35	661	25	2430	13
	various	290	22	0	.	0	.	0	.	0	.	111	47	65	36	304	43	78	38	0	.	143	39	283	38	160	30	221	29	1654	13
	indeterminable	862	17	0	.	4	100	0	.	0	.	660	21	638	18	441	20	413	24	550	36	271	24	972	16	1385	16	1389	17	7586	6
	wood harvest	260	28	0	.	9	100	0	.	0	.	110	44	58	37	87	37	62	65	9	71	10	100	84	58	110	42	8	71	807	15
	other human activity	93	35	0	.	0	.	0	.	0	.	166	47	80	44	99	42	34	59	0	.	10	100	78	79	74	52	37	51	672	19
	weather	75	39	0	.	10	100	10	100	0	.	63	47	225	26	111	42	102	39	46	85	102	48	247	31	190	32	408	23	1589	11
	lying living trees	27	86	0	.	4	100	0	.	0	.	0	.	44	51	18	71	30	58	51	60	39	61	72	45	59	62	103	47	447	20
	dead trees	686	21	0	.	18	62	0	.	0	.	969	22	1188	23	708	18	768	22	309	33	510	27	1123	18	1082	18	1495	22	8857	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15113	8	38	100	426	61	132	73	197	50	3835	18	7058	11	6376	13	2649	19	2259	23	1934	26	4001	15	4080	15	8191	12	56289	4
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	4	100	0	.	0	.	0	.	0	.	90	76	107	65
	rockfall	484	35	0	.	0	.	0	.	0	.	201	47	523	39	262	55	463	35	323	37	254	41	531	39	289	39	1010	22	4340	12
	various	465	27	0	.	30	88	0	.	8	71	107	36	195	24	259	40	66	36	51	60	79	42	146	51	182	34	224	25	1812	12
	indeterminable	1707	11	10	100	33	71	0	.	61	61	664	19	968	15	636	18	417	24	444	24	311	29	705	19	808	20	1298	15	8062	5
	wood harvest	362	20	0	.	0	.	0	.	8	71	100	52	457	24	291	33	63	59	38	65	32	67	20	100	18	71	10	100	1398	13
	other human activity	332	22	0	.	0	.	0	.	0	.	90	69	114	37	193	33	77	51	14	78	48	68	99	58	65	45	204	45	1236	14
	weather	175	32	0	.	0	.	0	.	0	.	43	51	166	32	45	51	60	58	139	46	46	73	170	29	255	43	303	24	1402	13
	lying living trees	21	71	0	.	0	.	0	.	0	.	19	71	114	58	8	100	19	71	0	.	0	.	109	45	14	76	49	52	353	26
	dead trees	1316	13	10	100	18	80	33	89	14	76	795	20	989	18	582	20	529	29	385	26	312	32	1133	20	683	20	1362	16	8163	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11418	9	1742	24	2375	22	1941	26	1626	31	2308	21	9322	10	8104	12	2159	28	1418	30	1824	24	3447	17	2676	18	6212	13	56574	4
	fire	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	4	100	0	.	0	.	10	100	0	.	59	100	82	74
	rockfall	932	27	36	77	43	100	0	.	141	87	0	.	163	86	72	87	246	44	349	47	651	29	323	41	145	44	681	28	3782	13
	various	311	31	55	52	54	56	41	61	38	69	62	42	291	21	213	29	45	43	37	68	98	50	80	52	98	33	281	36	1705	11
	indeterminable	1848	12	241	34	212	32	119	36	255	41	261	27	1012	15	1269	17	295	28	224	37	408	36	513	23	499	21	1924	15	9080	6
	wood harvest	567	20	54	62	39	79	79	46	44	66	113	47	339	36	156	39	36	90	30	71	89	42	46	81	42	60	0	.	1632	13
	other human activity	369	38	59	64	13	76	17	50	0	.	32	87	223	42	212	43	48	81	9	100	28	63	19	71	86	51	95	57	1210	18
	weather	125	31	0	.	25	71	73	67	26	86	4	100	166	32	122	47	50	47	19	63	76	61	189	36	108	40	199	31	1180	13
	lying living trees	29	58	0	.	0	.	0	.	10	100	10	100	31	58	9	100	30	74	10	100	27	63	59	48	97	41	109	38	421	18
	dead trees	1163	14	172	32	176	29	301	40	148	38	239	33	1128	18	778	19	375	26	385	32	538	26	1057	22	540	25	1420	20	8419	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	12894	8	2755	17	6185	12	8740	11	9377	10	924	33	5560	13	4696	15	1553	27	946	33	909	34	1920	24	1809	21	5269	14	63538	4
	fire	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	334	63	344	62
	rockfall	283	32	248	49	0	.	30	100	0	.	0	.	199	49	71	40	213	57	194	43	148	47	236	42	51	68	210	35	1884	15
	various	308	36	55	39	135	29	184	46	194	36	31	55	103	37	80	60	26	74	70	100	37	79	18	71	112	43	509	35	1863	14
	indeterminable	1532	13	423	20	736	18	897	14	1082	16	94	46	832	16	544	22	120	41	106	42	57	40	241	30	359	27	1783	17	8806	6
	wood harvest	526	22	154	34	328	26	327	22	363	21	44	100	308	29	73	49	0	.	0	.	0	.	29	74	56	87	39	79	2249	10
	other human activity	173	32	69	71	35	58	82	39	96	37	8	100	206	46	108	63	0	.	9	100	0	.	14	77	10	100	51	53	861	17
	weather	256	31	9	100	67	41	62	46	100	34	10	100	141	32	70	60	19	71	26	58	41	63	117	47	72	58	211	25	1202	12
	lying living trees	32	52	0	.	0	.	10	100	48	52	10	100	0	.	22	71	0	.	0	.	19	100	61	53	33	67	75	62	310	24
	dead trees	1526	13	330	30	510	21	657	20	930	15	50	74	577	23	613	23	230	31	133	68	189	43	650	24	275	30	1702	18	8371	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5815	12	8807	10	5580	13	11242	8	23341	6	0	.	1437	32	1489	24	844	43	638	45	191	50	1413	29	132	65	7070	12	67999	3
	fire	60	100	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	252	54	319	47
	rockfall	143	86	41	61	0	.	0	.	0	.	0	.	0	.	356	76	384	72	41	55	24	100	92	87	0	.	514	29	1594	28
	various	137	33	184	24	83	38	228	28	383	21	0	.	4	100	53	43	33	58	17	63	50	100	0	.	52	93	465	25	1690	11
	indeterminable	645	16	1140	13	504	20	1062	12	2437	8	0	.	103	39	330	34	337	51	75	53	18	65	148	44	13	100	2631	15	9443	6
	wood harvest	313	30	208	23	118	32	508	18	676	15	0	.	4	100	44	71	21	100	13	100	11	100	0	.	0	.	30	71	1946	9
	other human activity	56	38	69	44	102	48	178	33	372	30	0	.	4	100	64	55	70	87	4	100	0	.	9	71	0	.	211	33	1139	15
	weather	146	29	58	53	166	43	149	26	146	27	0	.	15	76	9	100	20	100	0	.	0	.	45	72	4	100	257	26	1015	13
	lying living trees	15	79	13	76	22	71	14	77	63	45	0	.	10	100	12	100	0	.	10	100	0	.	0	.	0	.	65	47	225	23
	dead trees	870	17	787	18	765	19	633	16	1263	11	0	.	184	46	228	35	93	47	67	70	38	61	301	39	24	100	3138	14	8392	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52508	3	13342	8	15307	8	22166	6	34540	5	14905	9	34493	5	26169	6	18725	8	9220	11	10037	10	36997	5	48124	4	48982	3	385515	1
	fire	60	100	14	78	0	.	4	100	10	100	0	.	4	100	14	100	17	63	0	.	10	100	28	54	4	100	751	35	914	30
	rockfall	1867	18	325	39	43	100	30	100	141	87	544	30	1269	23	832	38	1839	21	1144	21	1292	20	2156	15	1340	15	3750	10	16571	6
	various	1560	13	295	19	318	21	453	24	622	17	481	25	772	12	934	20	478	16	199	42	469	22	1165	15	1501	11	2138	12	11385	5
	indeterminable	6683	5	1814	10	1546	12	2078	9	3835	7	2321	10	4470	6	3426	9	3251	9	2029	14	1347	15	7091	6	9079	5	11411	5	60380	2
	wood harvest	2032	10	416	19	494	20	914	13	1092	12	413	25	1250	16	654	19	246	29	104	36	146	31	288	29	460	22	98	40	8607	5
	other human activity	1023	17	197	35	150	36	277	24	467	25	474	30	696	21	707	20	392	25	138	52	121	41	472	25	580	20	658	20	6352	7
	weather	791	15	68	48	286	28	294	23	272	21	289	22	1046	13	455	21	661	17	361	28	323	25	1821	10	1727	12	2690	8	11086	4
	lying living trees	122	31	13	76	25	62	24	61	120	32	55	42	235	32	104	32	142	28	164	45	147	38	759	18	927	21	678	18	3515	9
	dead trees	5686	6	1299	14	1587	12	1624	13	2356	9	3030	11	5496	9	3431	10	4348	10	1541	15	2147	13	8772	7	7905	7	11122	6	60343	2
	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. 1426638. Birmensdorf, Swiss Federal Research Institute WSL

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