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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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	.	0	.	76	100	0	.	739	53	0	.	0	.	6313	17	9591	13	2042	26	18762	9
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	44	86	51	99	57	353	29
	various	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	18	100	0	.	0	.	107	47	350	32	14	71	523	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	186	49	0	.	0	.	1118	19	1850	16	450	35	3629	11
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	31	64	0	.	44	50
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	9	100	0	.	0	.	28	62	44	58	40	100	134	39
	weather	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	216	35	344	27	104	52	689	19
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	49	482	50	0	.	629	40
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	205	100	0	.	175	56	0	.	0	.	1437	22	1642	20	329	33	3787	14
	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	.	835	55	2820	38	307	100	1978	28	400	48	914	51	6472	20	6866	15	4415	18	25007	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	.	7	100	67	58	0	.	56	53	34	100	0	.	87	50	97	55	105	62	453	25
	various	0	.	0	.	0	.	0	.	0	.	0	.	44	100	0	.	71	47	0	.	0	.	187	45	210	38	76	57	588	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	177	61	195	45	0	.	475	31	251	54	126	63	730	21	1339	19	717	30	4010	11
	wood harvest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	77	149	65	7	100	255	48
	other human activity	0	.	0	.	0	.	0	.	0	.	7	100	24	100	0	.	24	77	0	.	0	.	102	45	188	68	6	100	351	39
	weather	0	.	0	.	0	.	0	.	0	.	12	100	75	46	0	.	110	48	0	.	7	100	360	31	40	55	195	38	799	19
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	42	85	22	100	0	.	0	.	0	.	71	62	44	61	23	76	202	34
	dead trees	0	.	0	.	0	.	0	.	0	.	194	60	379	42	123	100	936	39	72	82	86	59	1653	31	1074	22	343	33	4859	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	703	38	0	.	459	55	0	.	0	.	2486	34	3567	20	1136	33	3960	22	1340	40	810	40	5696	18	9874	14	6567	19	36599	7
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	18	100
	rockfall	0	.	0	.	0	.	0	.	0	.	70	100	62	58	0	.	265	53	7	100	18	100	334	57	209	45	163	65	1127	25
	various	30	100	0	.	30	81	0	.	0	.	99	53	22	75	0	.	149	41	15	100	61	77	91	45	197	43	149	36	844	18
	indeterminable	64	53	0	.	68	61	0	.	0	.	445	31	544	28	193	44	755	24	334	48	112	41	981	22	1679	20	889	24	6063	9
	wood harvest	0	.	0	.	0	.	0	.	0	.	25	73	147	57	6	100	69	61	0	.	0	.	31	100	150	47	0	.	427	29
	other human activity	0	.	0	.	0	.	0	.	0	.	229	72	23	79	36	72	41	71	177	69	0	.	97	72	156	41	7	100	768	30
	weather	27	100	0	.	33	70	0	.	0	.	136	49	287	33	74	58	249	38	0	.	18	100	316	35	230	34	315	40	1686	14
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	22	100	46	75	43	61	0	.	35	71	248	64	17	100	42	72	453	38
	dead trees	44	61	0	.	127	76	0	.	0	.	801	38	866	40	616	62	1365	28	176	50	198	53	1146	25	1411	23	662	24	7413	11
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1201-1400 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5953	15	0	.	248	75	0	.	0	.	3814	23	3968	21	3378	20	3617	23	1688	33	1787	31	5678	20	4582	20	5433	19	40144	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	rockfall	8	100	0	.	0	.	0	.	0	.	429	54	295	56	84	85	294	46	88	75	173	70	335	41	174	51	674	34	2553	18
	various	211	35	0	.	0	.	0	.	0	.	113	71	15	71	171	30	69	48	0	.	95	77	122	40	61	66	132	41	989	17
	indeterminable	527	22	0	.	8	100	0	.	0	.	583	30	566	27	499	26	423	32	259	55	317	33	768	24	874	28	870	21	5694	9
	wood harvest	142	46	0	.	0	.	0	.	0	.	165	48	45	53	48	53	51	77	17	71	19	100	61	77	100	63	0	.	648	22
	other human activity	35	71	0	.	0	.	0	.	0	.	141	71	27	63	164	43	35	71	0	.	17	100	0	.	64	55	69	51	552	25
	weather	49	61	0	.	17	100	0	.	0	.	71	62	248	33	173	45	33	71	14	100	116	68	194	33	75	49	241	47	1231	16
	lying living trees	46	85	0	.	7	100	0	.	0	.	0	.	40	73	15	100	0	.	35	100	49	74	35	71	18	100	112	71	358	31
	dead trees	635	31	0	.	25	76	0	.	0	.	1192	27	1310	34	686	24	599	26	281	44	428	37	837	24	1125	24	630	32	7748	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	14702	10	0	.	651	70	215	75	188	59	3174	26	7008	15	4986	19	2171	29	2274	30	2228	35	3650	22	2371	27	5721	19	49340	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	7	100	0	.	0	.	0	.	0	.	139	83	170	69
	rockfall	539	38	0	.	0	.	0	.	0	.	0	.	896	39	219	53	378	51	379	47	238	57	325	44	471	43	973	30	4418	14
	various	288	30	0	.	52	88	0	.	13	71	67	54	185	32	157	35	62	52	69	70	62	50	48	59	203	43	153	36	1358	13
	indeterminable	1483	14	0	.	57	71	0	.	47	63	429	27	757	19	484	24	262	33	451	32	295	37	569	28	651	28	775	24	6259	7
	wood harvest	301	27	0	.	0	.	0	.	14	71	61	65	449	31	173	49	39	71	37	100	51	76	0	.	18	100	16	100	1159	17
	other human activity	318	28	0	.	0	.	0	.	0	.	0	.	80	54	138	49	0	.	0	.	62	88	32	63	61	67	51	87	742	20
	weather	166	47	0	.	0	.	0	.	0	.	46	75	231	35	48	73	69	71	63	60	60	89	65	53	94	61	251	34	1093	17
	lying living trees	17	100	0	.	0	.	0	.	0	.	15	100	67	60	0	.	18	100	0	.	0	.	119	64	7	100	18	100	261	35
	dead trees	1181	16	0	.	31	79	58	89	17	100	684	29	1029	20	654	28	567	40	405	36	313	43	718	25	320	35	1125	26	7103	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	12904	11	1453	32	1908	37	2494	30	1229	44	2019	32	9028	14	7996	15	1740	28	1401	40	1259	32	2864	25	2691	25	5604	19	54592	6
	fire	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	108	100	143	78
	rockfall	1011	37	17	100	0	.	0	.	34	100	0	.	32	71	0	.	223	59	421	59	653	39	300	67	51	74	287	37	3029	19
	various	379	43	7	100	17	100	46	87	7	100	50	53	242	30	220	34	6	100	8	100	93	76	0	.	98	39	303	46	1477	17
	indeterminable	1774	16	217	49	138	47	145	46	126	48	248	37	826	22	1257	23	360	35	205	42	293	34	435	34	563	28	1769	23	8356	8
	wood harvest	417	28	66	78	16	70	102	57	66	72	113	69	317	59	179	53	0	.	52	71	88	59	58	100	42	72	0	.	1516	19
	other human activity	263	46	7	100	0	.	21	58	0	.	50	100	332	47	309	49	0	.	15	100	26	78	0	.	62	58	26	100	1111	23
	weather	86	50	0	.	24	100	117	77	7	100	0	.	152	43	107	80	68	54	8	100	77	92	113	59	131	53	156	45	1045	20
	lying living trees	34	71	0	.	0	.	0	.	17	100	18	100	36	72	15	100	55	74	0	.	22	100	69	61	53	74	115	47	435	24
	dead trees	1055	19	174	43	107	43	245	51	91	65	128	57	1252	23	730	27	444	34	449	40	499	37	725	34	447	35	1279	24	7626	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	11575	12	2701	24	5342	19	9859	13	9639	13	895	47	6288	16	4516	21	1641	30	566	54	1318	39	1069	48	2188	23	5524	19	63121	5
	fire	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	588	66	605	64
	rockfall	210	48	323	59	0	.	54	100	0	.	0	.	171	73	64	53	181	58	128	59	165	56	267	50	92	69	261	45	1916	18
	various	189	33	59	50	87	49	279	55	142	31	28	79	96	54	77	91	37	85	0	.	62	79	15	100	137	52	719	43	1925	20
	indeterminable	1587	17	329	31	652	26	1077	17	811	19	95	61	796	21	733	27	179	46	54	70	63	50	193	52	557	29	1703	22	8830	7
	wood harvest	556	32	220	41	282	31	428	26	493	23	0	.	380	39	50	87	0	.	0	.	0	.	18	100	104	87	18	100	2548	13
	other human activity	95	39	17	100	0	.	69	47	100	40	0	.	171	46	71	79	0	.	15	100	0	.	25	76	18	100	27	78	609	20
	weather	124	36	0	.	69	54	104	49	94	41	18	100	158	44	53	74	31	70	33	71	37	100	119	55	97	71	229	34	1165	15
	lying living trees	7	100	0	.	0	.	0	.	67	61	18	100	0	.	39	71	0	.	0	.	31	100	18	100	25	77	96	85	299	35
	dead trees	1352	18	308	43	409	34	743	23	968	20	61	100	551	32	606	28	245	36	15	100	94	59	441	38	339	37	1699	22	7832	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5748	16	8626	13	5304	18	11324	11	21789	8	0	.	1137	52	1557	32	923	56	787	60	169	70	1340	40	111	100	7321	17	66136	4
	fire	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	256	57	263	56
	rockfall	225	100	59	72	0	.	0	.	0	.	0	.	0	.	619	75	0	.	54	64	0	.	24	100	0	.	451	37	1432	38
	various	114	46	169	29	55	48	133	40	233	27	0	.	0	.	51	61	35	84	23	75	0	.	0	.	0	.	237	52	1050	16
	indeterminable	595	23	1214	18	526	27	922	17	2291	12	0	.	18	71	480	39	127	60	63	89	12	70	124	49	0	.	2856	22	9227	8
	wood harvest	327	44	236	30	138	39	584	22	676	22	0	.	0	.	38	100	0	.	0	.	18	100	0	.	0	.	0	.	2017	13
	other human activity	67	48	51	75	55	52	139	49	375	48	0	.	0	.	84	71	129	87	0	.	0	.	8	100	0	.	212	45	1119	23
	weather	160	41	56	77	226	55	243	27	129	39	0	.	8	100	0	.	0	.	0	.	0	.	8	100	0	.	190	43	1021	18
	lying living trees	19	100	24	77	17	100	17	100	88	54	0	.	0	.	21	100	0	.	17	100	0	.	0	.	0	.	92	53	295	27
	dead trees	957	23	679	24	728	27	565	20	1088	16	0	.	43	60	189	40	60	64	38	71	46	74	126	47	0	.	2566	18	7084	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	51585	4	12780	10	13913	11	23892	8	32845	6	13223	12	33892	7	23875	8	16769	10	8456	15	8486	15	33082	7	38275	6	42628	5	353699	2
	fire	0	.	17	100	0	.	7	100	17	100	0	.	0	.	24	100	7	100	0	.	0	.	43	61	0	.	1098	40	1214	37
	rockfall	1993	25	399	49	0	.	54	100	34	100	506	48	1522	26	986	49	1396	23	1110	29	1246	26	1840	20	1181	22	3013	14	15281	8
	various	1210	17	235	24	242	31	457	37	394	19	358	30	639	19	675	19	447	21	115	47	373	34	570	21	1255	16	1782	20	8753	7
	indeterminable	6030	7	1760	15	1449	16	2144	11	3274	9	1978	14	3728	9	3645	12	2767	12	1617	19	1217	17	4917	9	7512	8	10030	8	52069	3
	wood harvest	1743	15	522	24	435	23	1113	16	1249	15	364	32	1338	21	494	29	159	40	106	50	176	40	281	40	594	28	41	61	8615	7
	other human activity	778	21	75	56	55	52	229	33	475	39	427	47	672	27	802	25	239	50	208	60	105	57	291	31	592	27	438	28	5385	9
	weather	610	20	56	77	370	37	464	26	230	28	283	32	1184	15	454	29	562	23	119	40	315	40	1391	15	1010	17	1683	14	8729	6
	lying living trees	124	43	24	77	24	77	17	100	171	38	51	58	207	34	158	36	116	45	51	75	137	42	707	28	647	38	498	28	2932	13
	dead trees	5224	8	1161	19	1427	18	1612	15	2165	12	3059	16	5636	13	3604	15	4390	14	1437	20	1665	18	7083	11	6359	10	8631	8	53452	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. 1426623. Birmensdorf, Swiss Federal Research Institute WSL

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