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
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 2004/06
		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	.	257	83	318	63	0	.	652	36	83	72	122	72	7383	11	11066	9	2932	14	22813	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	15	76	0	.	19	63
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	4	100	634	60	207	36	108	36	973	40
	various	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	15	73	20	100	0	.	118	32	394	20	70	38	622	15
	indeterminable	0	.	0	.	0	.	0	.	0	.	84	73	70	72	0	.	188	38	11	100	10	100	1356	14	1863	11	561	19	4143	8
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	56	80	42	0	.	103	35
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	5	100	0	.	0	.	104	70	176	36	22	100	316	31
	weather	0	.	0	.	0	.	0	.	0	.	9	100	34	63	0	.	52	53	76	100	10	100	331	21	532	17	250	23	1294	12
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	50	350	45	62	51	494	33
	dead trees	0	.	0	.	0	.	0	.	0	.	43	100	155	87	0	.	163	39	0	.	0	.	1174	17	2035	17	349	23	3920	11
	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	.	599	42	1945	28	226	71	2269	20	390	49	935	36	7246	14	10213	11	4313	14	28137	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	13	100	17	80
	rockfall	0	.	0	.	0	.	0	.	0	.	25	100	145	80	0	.	94	61	8	100	14	100	183	46	458	34	160	41	1087	21
	various	0	.	0	.	0	.	0	.	0	.	72	80	38	44	0	.	61	40	8	71	19	71	221	21	329	19	221	38	971	14
	indeterminable	0	.	0	.	0	.	0	.	0	.	227	47	313	31	53	77	584	20	98	45	179	36	1444	14	1994	12	937	15	5829	7
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	4	100	0	.	0	.	24	61	109	34	4	100	152	27
	other human activity	0	.	0	.	0	.	0	.	0	.	8	71	36	77	0	.	14	77	0	.	4	100	93	41	315	41	8	71	479	29
	weather	0	.	0	.	0	.	0	.	0	.	31	62	111	40	0	.	135	27	24	72	14	78	168	32	302	26	161	31	945	13
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	44	100	11	100	103	100	0	.	0	.	21	100	94	49	10	100	282	44
	dead trees	0	.	0	.	0	.	0	.	0	.	172	43	448	38	66	100	692	33	65	67	145	41	1386	22	1565	14	470	21	5009	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	693	26	0	.	436	41	0	.	0	.	2437	23	3697	17	1410	24	3502	15	950	31	781	33	5919	13	8491	11	6651	12	34968	5
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	10	100	14	76
	rockfall	0	.	0	.	0	.	0	.	0	.	50	45	44	59	0	.	285	43	66	53	24	71	253	33	258	29	170	38	1149	16
	various	43	41	0	.	14	79	0	.	0	.	91	38	275	50	10	100	137	30	32	69	14	76	90	35	316	34	163	29	1186	16
	indeterminable	147	35	0	.	7	100	0	.	0	.	595	25	863	19	59	38	1093	17	182	34	157	35	1181	15	1971	14	1234	15	7488	6
	wood harvest	0	.	0	.	11	100	0	.	0	.	53	68	22	73	12	77	89	51	4	100	13	76	60	48	183	28	0	.	447	19
	other human activity	21	70	0	.	0	.	0	.	0	.	243	45	73	52	0	.	100	36	43	62	0	.	80	46	131	36	58	56	749	19
	weather	18	63	0	.	22	61	0	.	0	.	174	31	221	32	47	48	186	31	95	42	0	.	290	29	272	50	176	32	1500	14
	lying living trees	0	.	0	.	0	.	0	.	0	.	21	71	32	100	4	100	145	59	0	.	10	100	156	55	10	100	42	63	420	31
	dead trees	62	40	0	.	80	66	0	.	0	.	871	28	680	23	578	44	1263	22	129	42	446	36	1182	20	1077	17	641	18	7008	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	6285	12	0	.	291	56	98	100	0	.	4051	18	3997	15	3576	14	3631	18	2039	24	2120	25	5256	14	5129	15	6649	12	43122	5
	fire	0	.	0	.	0	.	0	.	0	.	18	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	123	65	152	55
	rockfall	101	52	0	.	0	.	0	.	0	.	322	37	268	45	133	51	114	51	163	36	169	57	265	31	113	39	673	23	2321	13
	various	335	22	0	.	14	76	0	.	0	.	168	41	232	28	370	52	119	29	73	42	54	46	220	35	216	24	226	28	2027	13
	indeterminable	1096	15	0	.	8	71	4	100	0	.	877	26	809	16	588	19	793	20	324	37	420	26	861	18	1331	17	1448	15	8558	6
	wood harvest	178	35	0	.	0	.	0	.	0	.	68	40	139	27	21	71	4	100	5	100	18	71	16	62	203	28	10	100	661	15
	other human activity	113	33	0	.	0	.	0	.	0	.	230	51	178	34	134	46	47	53	10	100	10	100	53	52	97	45	99	39	971	17
	weather	111	33	0	.	23	84	20	100	0	.	195	30	302	26	128	33	95	43	42	61	69	74	135	36	193	26	212	35	1525	11
	lying living trees	4	100	0	.	0	.	0	.	0	.	0	.	34	52	0	.	20	71	51	60	119	59	86	44	0	.	124	38	438	23
	dead trees	389	21	0	.	14	76	0	.	0	.	919	23	1101	25	742	19	623	24	276	34	512	28	797	18	1074	19	1161	24	7608	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15244	7	38	100	516	56	115	75	204	54	3688	17	6958	12	6258	13	2690	19	2050	22	2156	29	4229	15	3547	17	8095	12	55789	4
	fire	0	.	0	.	0	.	0	.	4	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	65	139	60
	rockfall	452	34	0	.	26	86	0	.	0	.	170	44	470	37	281	47	237	37	288	40	196	47	557	35	398	33	667	23	3743	11
	various	431	19	0	.	9	100	0	.	26	62	95	35	397	19	203	27	140	35	101	56	84	53	104	40	272	29	388	20	2251	9
	indeterminable	1805	13	20	100	44	72	14	77	48	55	685	20	1077	15	709	20	626	24	432	29	455	26	692	19	964	21	1778	14	9348	6
	wood harvest	571	20	0	.	11	100	0	.	12	74	202	44	487	23	238	27	10	100	52	52	27	65	9	100	22	55	37	59	1677	12
	other human activity	167	22	0	.	11	100	0	.	4	100	154	54	236	37	181	40	101	48	47	77	49	62	107	60	267	30	85	42	1409	14
	weather	258	25	0	.	0	.	4	100	0	.	152	30	233	29	185	28	76	47	52	51	96	53	100	41	159	35	320	26	1636	11
	lying living trees	21	70	0	.	0	.	0	.	0	.	9	100	43	54	11	100	23	85	12	100	21	100	136	54	20	71	29	58	324	27
	dead trees	1092	14	0	.	9	70	29	100	0	.	717	21	859	17	519	22	398	35	244	31	262	34	758	24	625	24	1262	18	6773	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11869	9	1748	23	2429	24	1816	25	1682	33	2319	21	8189	11	8291	12	1938	34	1160	31	1883	22	4094	17	2672	19	6450	13	56541	4
	fire	9	100	0	.	4	100	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	58	82	82	60
	rockfall	893	29	48	100	50	100	0	.	62	100	21	100	258	61	156	57	197	53	398	44	604	31	172	47	98	61	272	32	3229	14
	various	517	28	31	56	86	49	67	52	28	73	43	47	428	27	453	53	73	43	8	71	115	36	99	38	79	36	578	37	2606	15
	indeterminable	1705	13	189	37	200	30	217	36	292	36	470	25	1233	18	1142	17	421	29	95	40	472	31	488	26	702	20	1950	16	9577	6
	wood harvest	821	17	67	51	90	44	75	58	54	59	106	44	252	27	281	30	39	79	10	100	75	56	40	74	22	71	22	100	1954	11
	other human activity	464	29	45	68	39	50	34	52	0	.	102	45	483	38	171	40	20	71	9	100	72	61	39	81	77	43	55	63	1609	16
	weather	285	22	10	100	33	49	61	46	13	76	44	44	206	23	252	31	24	61	56	62	40	49	48	56	30	58	100	37	1200	11
	lying living trees	30	74	10	100	0	.	10	100	10	100	10	100	45	46	15	77	20	100	0	.	0	.	9	100	43	50	59	61	261	23
	dead trees	799	16	149	36	118	42	194	38	144	37	264	33	821	18	749	20	301	30	298	39	312	32	662	24	451	27	1035	22	6295	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	12476	8	3250	18	6075	13	8559	10	10929	10	965	35	5545	13	5360	14	1916	26	810	33	1144	34	1778	23	2003	22	5632	13	66441	4
	fire	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	164	60	173	57
	rockfall	392	29	264	49	28	100	23	60	0	.	10	100	284	48	124	81	187	72	190	52	476	55	199	41	83	86	219	38	2480	17
	various	416	22	50	41	137	27	215	27	254	21	24	71	243	31	58	48	40	61	40	80	52	53	63	35	72	43	186	40	1848	10
	indeterminable	1441	13	411	22	785	16	648	17	1302	14	68	52	661	19	624	21	280	27	87	63	447	68	339	31	385	26	1980	17	9457	6
	wood harvest	665	17	92	38	441	21	478	21	450	23	26	65	243	27	155	37	28	75	0	.	54	100	23	84	65	86	69	59	2787	9
	other human activity	262	23	47	68	118	40	165	46	90	37	19	73	157	40	63	72	41	91	29	75	0	.	16	74	27	54	47	42	1081	14
	weather	412	18	27	54	104	33	201	27	204	23	41	53	225	27	98	47	12	58	0	.	14	78	71	49	43	56	189	31	1642	9
	lying living trees	0	.	0	.	0	.	15	78	23	61	21	100	0	.	4	100	0	.	0	.	4	100	46	54	14	76	0	.	126	31
	dead trees	1261	14	331	29	507	23	472	25	949	16	104	62	459	25	533	26	230	39	101	76	150	46	426	28	247	27	1385	18	7155	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6214	12	8488	11	6314	13	12642	8	23140	6	0	.	1381	31	2125	23	975	40	741	41	211	53	1163	30	298	51	7864	11	71557	3
	fire	0	.	0	.	4	100	8	100	25	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	225	54	262	47
	rockfall	157	72	79	76	0	.	0	.	0	.	0	.	0	.	243	86	70	100	18	62	0	.	141	77	0	.	188	36	896	32
	various	155	29	224	27	171	31	342	21	362	19	0	.	0	.	0	.	69	79	0	.	0	.	31	71	0	.	219	26	1574	10
	indeterminable	801	20	1314	14	851	23	1548	12	2830	9	0	.	134	37	271	34	70	62	114	44	27	58	255	40	0	.	2310	13	10524	5
	wood harvest	205	27	256	20	277	39	702	17	1374	12	0	.	0	.	52	46	0	.	24	100	0	.	0	.	0	.	4	100	2896	8
	other human activity	240	40	265	35	149	36	253	35	463	28	0	.	15	76	15	100	44	100	28	100	62	100	55	56	16	100	195	28	1799	13
	weather	239	24	95	33	102	35	259	25	254	19	0	.	25	85	55	51	4	100	0	.	0	.	24	71	0	.	411	26	1467	11
	lying living trees	15	79	4	100	47	52	8	71	90	41	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	68	48	242	24
	dead trees	581	19	576	21	792	21	535	18	1247	15	0	.	129	57	267	39	49	54	78	53	22	100	300	37	20	100	2241	16	6838	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52781	3	13523	8	16061	8	23229	6	35954	5	14316	9	32031	5	27247	6	17574	8	8224	11	9352	11	37069	5	43419	5	48586	3	379366	1
	fire	9	100	10	100	9	70	8	100	29	64	27	74	21	71	0	.	0	.	4	100	0	.	8	71	15	76	719	28	858	24
	rockfall	1996	17	390	38	104	59	23	60	62	100	599	24	1487	21	937	31	1185	21	1130	21	1487	23	2404	20	1615	16	2458	11	15877	6
	various	1896	11	306	21	432	18	624	16	670	14	494	21	1617	13	1095	28	654	16	283	28	337	22	947	13	1678	11	2052	13	13086	5
	indeterminable	6996	6	1935	11	1895	13	2431	9	4471	7	3005	11	5160	7	3445	9	4054	9	1342	15	2167	18	6615	6	9210	6	12197	5	64924	2
	wood harvest	2441	9	415	17	829	18	1255	13	1891	10	454	24	1153	13	759	16	173	34	95	40	186	39	195	26	684	16	145	36	10677	4
	other human activity	1268	14	357	29	316	24	453	26	557	24	757	25	1188	19	563	22	372	24	166	35	197	42	548	22	1106	16	568	16	8413	6
	weather	1323	10	132	27	284	20	544	17	471	14	646	15	1357	11	765	15	584	16	346	29	242	32	1168	12	1530	13	1819	10	11209	4
	lying living trees	70	42	14	77	47	52	33	50	123	33	61	47	198	33	44	44	311	44	73	47	153	48	534	24	531	31	394	21	2587	11
	dead trees	4183	7	1056	15	1519	14	1229	14	2341	10	3091	12	4653	10	3454	11	3719	12	1192	17	1849	15	6684	8	7094	7	8543	7	50607	3
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1426614. Birmensdorf, Swiss Federal Research Institute WSL

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