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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	.	0	.	52	100	0	.	638	53	0	.	0	.	5980	16	10113	14	1892	26	18675	9
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	341	35	25	77	82	60	448	29
	various	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	74	0	.	0	.	139	45	266	30	30	63	462	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	76	100	0	.	175	52	0	.	0	.	994	19	1577	16	394	37	3216	11
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	46	0	.	67	46
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	9	100	0	.	0	.	151	87	114	40	40	100	328	44
	weather	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	39	71	0	.	0	.	252	33	484	23	154	43	953	17
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	72	243	60	32	100	364	45
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	222	100	0	.	114	66	0	.	0	.	1212	23	1393	21	303	33	3244	15
	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	.	727	54	2574	37	241	100	1867	28	418	53	751	57	6661	21	6362	16	4059	19	23660	9
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	23	100
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	206	92	0	.	133	76	15	100	24	100	79	50	335	66	38	67	831	38
	various	0	.	0	.	0	.	0	.	0	.	15	72	7	100	0	.	70	49	14	71	0	.	158	36	213	33	361	43	839	22
	indeterminable	0	.	0	.	0	.	0	.	0	.	278	63	317	46	31	100	534	30	152	51	129	52	1216	22	1281	19	944	21	4881	10
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	17	100	113	47	0	.	137	41
	other human activity	0	.	0	.	0	.	0	.	0	.	16	71	17	100	0	.	17	100	0	.	0	.	55	63	57	52	7	100	169	31
	weather	0	.	0	.	0	.	0	.	0	.	42	73	142	52	0	.	145	34	44	72	7	100	175	44	168	49	72	46	795	19
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	72	100	22	100	0	.	0	.	0	.	35	100	0	.	18	100	147	58
	dead trees	0	.	0	.	0	.	0	.	0	.	186	62	454	57	136	100	935	41	66	90	104	65	1499	33	1181	23	371	31	4932	15
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	634	39	0	.	599	51	0	.	0	.	1849	35	3188	20	1369	36	2963	21	1160	42	684	44	5414	18	8863	15	6259	18	32979	7
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	rockfall	0	.	0	.	0	.	0	.	0	.	24	73	56	76	0	.	396	51	22	75	42	72	285	45	235	44	127	54	1186	23
	various	39	61	0	.	7	100	0	.	0	.	68	55	439	53	0	.	133	32	44	86	7	100	51	58	371	45	138	47	1295	23
	indeterminable	60	56	0	.	13	100	0	.	0	.	676	35	918	24	83	44	1035	22	181	50	149	49	1176	22	2221	19	1006	21	7518	9
	wood harvest	0	.	0	.	0	.	0	.	0	.	16	71	28	100	21	77	122	61	0	.	7	100	44	85	264	32	0	.	502	25
	other human activity	38	70	0	.	0	.	0	.	0	.	251	67	80	56	0	.	47	60	74	62	0	.	96	61	128	38	18	100	732	27
	weather	0	.	0	.	40	61	0	.	0	.	214	36	215	42	30	71	215	40	123	55	0	.	292	42	168	30	176	46	1473	15
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	17	100	0	.	18	100	189	78	17	100	0	.	250	60
	dead trees	44	61	0	.	127	76	0	.	0	.	973	39	580	30	657	66	1336	31	147	55	194	54	1206	27	1339	23	484	27	7087	12
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5076	16	0	.	220	79	0	.	0	.	3714	24	3701	20	3489	20	3339	23	1510	34	1826	37	5393	20	3586	22	4903	18	36757	7
	fire	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	193	72	223	64
	rockfall	92	78	0	.	0	.	0	.	0	.	489	41	257	53	124	59	143	66	141	53	171	74	224	44	141	49	645	33	2427	16
	various	306	33	0	.	25	76	0	.	0	.	91	40	326	35	134	35	156	35	100	52	48	66	129	45	184	38	251	35	1750	13
	indeterminable	899	23	0	.	8	100	0	.	0	.	669	24	724	23	628	24	870	26	212	48	406	39	871	25	1256	26	1243	21	7787	8
	wood harvest	181	44	0	.	0	.	0	.	0	.	79	48	81	51	18	100	7	100	9	100	31	71	14	100	135	43	0	.	554	21
	other human activity	63	59	0	.	0	.	0	.	0	.	175	73	137	55	169	57	60	68	18	100	0	.	17	100	169	47	126	46	935	22
	weather	106	48	0	.	34	100	0	.	0	.	161	47	195	42	169	38	98	48	40	71	101	84	109	47	207	34	65	43	1286	15
	lying living trees	8	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	35	100	171	71	23	100	0	.	54	100	306	46
	dead trees	403	29	0	.	25	76	0	.	0	.	1162	27	1119	38	786	24	502	28	226	46	518	39	684	25	1178	26	489	33	7093	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14210	10	0	.	589	78	174	81	217	63	3148	25	6654	16	5211	18	1799	33	2255	30	2592	38	3570	22	2228	28	5463	20	48111	6
	fire	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	100	120	94
	rockfall	545	29	0	.	36	100	0	.	0	.	38	70	798	37	202	49	201	52	360	51	161	68	447	44	590	39	651	33	4028	14
	various	324	25	0	.	16	100	0	.	27	79	44	48	364	26	223	34	60	67	126	71	83	84	81	51	237	46	310	29	1894	13
	indeterminable	1651	18	0	.	32	100	23	76	56	71	396	33	837	19	673	24	569	35	419	40	561	31	596	27	657	35	1169	24	7639	8
	wood harvest	593	26	0	.	20	100	0	.	14	100	246	66	476	32	204	42	18	100	50	76	20	73	15	100	0	.	31	100	1687	17
	other human activity	159	31	0	.	0	.	0	.	0	.	187	72	193	40	215	51	0	.	0	.	46	100	15	100	200	42	43	100	1058	21
	weather	265	33	0	.	0	.	7	100	0	.	163	37	252	35	117	44	84	67	83	54	63	89	51	79	136	57	287	38	1507	15
	lying living trees	19	100	0	.	0	.	0	.	0	.	15	100	33	71	0	.	35	100	0	.	35	100	83	82	17	100	0	.	237	39
	dead trees	1004	19	0	.	8	100	52	100	0	.	587	28	945	20	589	29	497	48	290	41	271	49	589	27	537	40	1043	26	6411	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	12935	11	1499	33	1697	38	2335	28	1237	42	1726	32	8400	15	8332	17	1316	28	1418	38	1393	30	3185	25	2821	26	5894	19	54188	6
	fire	17	100	0	.	8	100	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	22	100	65	53
	rockfall	1037	40	0	.	0	.	0	.	110	100	36	100	168	70	41	81	278	65	447	54	611	44	206	65	57	88	213	49	3202	19
	various	550	29	7	100	78	64	66	48	41	85	52	58	352	26	617	65	60	67	14	71	123	46	99	53	83	48	450	41	2594	19
	indeterminable	1707	18	215	47	182	42	212	36	200	51	368	42	1535	23	1054	20	478	37	135	48	327	41	390	34	794	26	1741	21	9339	8
	wood harvest	783	25	49	73	90	67	122	62	40	71	147	52	238	39	102	36	63	79	0	.	40	62	43	100	40	71	0	.	1758	15
	other human activity	292	39	0	.	17	100	24	76	0	.	172	47	655	47	157	63	17	100	15	100	62	91	7	100	47	59	0	.	1467	24
	weather	346	26	17	100	33	66	90	52	0	.	25	77	197	33	187	33	40	61	53	87	60	52	7	100	55	58	92	52	1202	13
	lying living trees	17	100	17	100	0	.	18	100	17	100	0	.	28	79	17	100	37	100	0	.	0	.	16	100	61	58	63	77	291	29
	dead trees	745	21	185	40	124	49	205	45	101	47	110	68	929	25	735	28	383	37	374	48	374	37	517	38	464	38	1078	22	6324	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
601-800 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11477	12	3152	26	4627	18	8973	13	11622	13	955	47	6419	17	4953	20	1862	29	567	52	1498	39	1000	43	2501	24	5752	18	65357	5
	fire	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	212	77	229	71
	rockfall	412	40	367	58	46	100	39	59	0	.	19	100	278	70	193	89	71	76	92	71	419	85	220	54	151	87	364	41	2671	21
	various	470	27	70	49	115	40	236	36	205	28	42	71	129	40	41	67	12	70	18	100	68	63	80	43	107	50	235	52	1828	13
	indeterminable	1545	17	379	34	694	24	834	22	1380	18	28	79	505	25	686	26	266	36	37	84	708	71	327	47	588	28	2032	23	10010	9
	wood harvest	627	25	53	75	228	34	591	25	379	24	32	81	284	37	87	61	13	71	0	.	90	100	0	.	17	100	20	100	2422	12
	other human activity	229	35	77	72	81	67	228	58	90	50	21	100	184	48	26	75	0	.	15	100	0	.	17	100	15	70	20	100	1003	20
	weather	425	23	41	61	111	45	302	30	238	30	64	58	195	39	123	59	13	71	0	.	17	100	32	63	41	61	198	41	1800	12
	lying living trees	0	.	0	.	0	.	7	100	23	77	36	100	0	.	7	100	0	.	0	.	0	.	54	74	25	76	0	.	151	40
	dead trees	1040	22	271	40	512	33	564	33	1161	21	89	100	390	33	601	28	182	37	0	.	205	55	252	47	292	31	1281	23	6839	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	6774	17	8501	15	5915	18	12969	11	19930	8	0	.	952	49	1971	31	1071	54	782	59	188	71	1199	37	104	100	8026	17	68382	5
	fire	0	.	0	.	0	.	14	100	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	275	70	316	61
	rockfall	229	91	139	75	0	.	0	.	0	.	0	.	0	.	420	86	0	.	15	100	0	.	0	.	0	.	177	39	980	44
	various	80	45	260	37	75	55	279	28	463	24	0	.	0	.	0	.	96	100	0	.	0	.	57	71	0	.	151	33	1462	14
	indeterminable	580	25	1644	17	682	24	1444	14	2594	12	0	.	66	71	380	39	56	100	135	59	28	71	194	51	0	.	2340	17	10142	7
	wood harvest	170	44	282	28	151	43	704	22	1432	15	0	.	0	.	73	52	0	.	0	.	0	.	0	.	0	.	7	100	2819	10
	other human activity	301	52	182	57	115	46	230	36	570	39	0	.	0	.	25	100	81	100	0	.	0	.	53	76	0	.	151	42	1708	19
	weather	235	35	109	35	90	49	318	31	271	26	0	.	7	100	43	73	6	100	0	.	0	.	25	100	0	.	549	30	1651	14
	lying living trees	20	100	7	100	40	61	7	100	109	49	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	83	62	284	29
	dead trees	643	24	627	26	814	29	613	24	1140	17	0	.	36	65	258	49	54	77	52	76	37	100	184	47	0	.	2090	21	6548	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	51106	4	13151	11	13648	11	24452	8	33006	6	12119	12	31940	7	25564	8	14853	10	8112	15	8932	16	32403	7	36576	6	42247	5	348109	2
	fire	17	100	17	100	8	100	14	100	34	82	30	100	18	100	0	.	0	.	7	100	0	.	7	100	0	.	838	37	991	32
	rockfall	2315	22	506	47	83	71	39	59	110	100	605	34	1763	25	979	42	1221	26	1091	29	1427	33	1802	18	1533	24	2298	15	15773	8
	various	1769	13	337	30	316	26	582	20	736	18	312	23	1617	18	1015	40	614	22	316	35	328	32	794	17	1461	16	1926	15	12124	6
	indeterminable	6442	8	2238	14	1611	15	2514	11	4230	9	2414	16	4979	10	3535	10	3984	11	1272	20	2307	25	5764	9	8374	8	10868	7	60532	3
	wood harvest	2354	13	384	25	488	24	1418	16	1865	13	519	35	1107	19	505	23	231	40	59	66	188	52	134	47	635	19	58	65	9946	6
	other human activity	1082	20	259	45	213	36	481	32	659	34	822	32	1281	26	593	30	232	42	122	44	108	67	411	38	730	19	405	26	7399	9
	weather	1377	13	168	29	308	26	716	20	509	20	669	20	1227	16	669	19	638	20	343	30	248	43	945	19	1259	15	1592	15	10668	5
	lying living trees	65	52	24	77	40	61	32	65	149	40	51	76	148	54	55	55	90	60	51	75	223	57	487	38	364	42	251	38	2030	16
	dead trees	3878	9	1083	19	1610	19	1433	18	2401	13	3107	17	4676	14	3763	16	4002	16	1155	23	1703	19	6144	12	6383	10	7140	8	48478	4
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1426608. Birmensdorf, Swiss Federal Research Institute WSL

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