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 NFI4/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	.	421	100	76	100	0	.	792	50	255	100	124	100	6606	16	12457	12	4383	17	25114	8
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	14	71
	rockfall	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	37	161	35	161	44	533	22
	various	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	18	100	0	.	0	.	107	47	409	29	33	65	601	22
	indeterminable	0	.	0	.	0	.	0	.	0	.	45	100	24	100	0	.	218	44	18	100	18	100	1271	18	2273	14	754	24	4620	10
	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	47	86	142	38
	weather	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	18	100	269	31	423	23	444	29	1178	16
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	202	41	499	48	32	64	733	35
	dead trees	0	.	0	.	0	.	0	.	0	.	0	.	205	100	0	.	209	49	18	100	0	.	1591	20	2046	18	412	28	4481	12
	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	3056	36	307	100	2673	24	418	46	975	48	7178	19	8734	13	4819	17	28995	8
	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	.	112	43	97	55	186	56	559	25
	various	0	.	0	.	0	.	0	.	0	.	0	.	63	76	0	.	71	47	0	.	0	.	201	42	228	36	76	57	639	22
	indeterminable	0	.	0	.	0	.	0	.	0	.	177	61	214	42	0	.	629	25	276	50	126	63	891	20	1584	17	759	28	4657	10
	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	81	76	0	.	38	61	0	.	7	100	225	52	212	61	6	100	576	32
	weather	0	.	0	.	0	.	0	.	0	.	12	100	75	46	0	.	204	53	0	.	7	100	440	27	58	49	280	34	1075	18
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	42	85	22	100	0	.	0	.	0	.	89	53	81	46	39	61	272	28
	dead trees	0	.	0	.	0	.	0	.	0	.	194	60	386	41	123	100	1005	37	72	82	86	59	1925	27	1179	20	456	27	5425	13
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1401-1600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	800	34	0	.	459	55	0	.	0	.	2716	32	3913	19	1408	30	4677	20	1471	36	1114	36	6100	17	11336	14	8155	16	42148	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	.	79	89	62	58	0	.	342	47	7	100	18	100	352	54	366	43	163	65	1388	23
	various	67	56	0	.	30	81	0	.	0	.	99	53	22	75	31	100	149	41	15	100	61	77	91	45	214	40	233	30	1013	16
	indeterminable	78	45	0	.	68	61	0	.	0	.	454	30	574	27	193	44	905	22	370	44	112	41	1069	21	1832	19	1010	22	6664	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	61	58	177	69	0	.	97	72	156	41	7	100	787	29
	weather	27	100	0	.	33	70	0	.	0	.	136	49	375	34	74	58	320	34	77	77	18	100	394	31	247	32	464	31	2167	13
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	22	100	46	75	43	61	36	100	35	71	300	55	17	100	58	59	558	33
	dead trees	44	61	0	.	127	76	0	.	0	.	819	37	866	40	616	62	1465	27	242	41	245	44	1181	24	1502	22	803	22	7910	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	6259	15	0	.	443	61	0	.	0	.	4025	22	4239	20	3917	19	3617	23	2432	28	2458	30	6331	18	4758	19	5699	18	44178	6
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	rockfall	8	100	0	.	0	.	0	.	0	.	429	54	295	56	84	85	294	46	202	65	338	61	335	41	174	51	729	32	2888	17
	various	218	34	0	.	0	.	0	.	0	.	113	71	31	50	179	29	69	48	0	.	95	77	377	49	61	66	132	41	1275	19
	indeterminable	527	22	0	.	8	100	0	.	0	.	665	27	584	26	532	25	423	32	523	54	351	31	894	23	953	26	1093	21	6553	9
	wood harvest	142	46	0	.	0	.	0	.	0	.	165	48	45	53	48	53	51	77	17	71	19	100	61	77	125	54	0	.	673	21
	other human activity	35	71	0	.	0	.	0	.	0	.	189	59	27	63	164	43	35	71	0	.	17	100	0	.	74	49	69	51	611	24
	weather	49	61	0	.	17	100	0	.	0	.	77	58	248	33	183	43	33	71	85	85	123	64	194	33	85	45	278	42	1372	16
	lying living trees	46	85	0	.	7	100	0	.	0	.	0	.	40	73	15	100	0	.	52	74	49	74	43	61	18	100	130	62	401	29
	dead trees	635	31	0	.	25	76	0	.	0	.	1198	27	1346	33	686	24	599	26	334	39	560	35	942	22	1226	23	682	30	8232	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
1001-1200 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	14710	10	0	.	651	70	215	75	188	59	3982	24	7457	16	5948	18	2413	27	2390	29	2242	35	4503	19	3392	23	6756	18	54847	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	349	41	471	43	973	30	4442	14
	various	288	30	0	.	52	88	0	.	13	71	67	54	185	32	157	35	77	46	69	70	62	50	48	59	275	37	174	33	1466	13
	indeterminable	1483	14	0	.	57	71	0	.	47	63	482	26	906	20	524	22	401	35	532	30	295	37	578	28	658	28	846	22	6808	7
	wood harvest	301	27	0	.	0	.	0	.	14	71	61	65	449	31	213	44	39	71	37	100	51	76	0	.	18	100	16	100	1199	17
	other human activity	318	28	0	.	0	.	0	.	0	.	94	100	97	48	200	46	0	.	0	.	62	88	32	63	82	56	51	87	935	20
	weather	166	47	0	.	0	.	0	.	0	.	46	75	231	35	48	73	69	71	151	63	77	72	82	47	199	60	318	33	1388	17
	lying living trees	17	100	0	.	0	.	0	.	0	.	33	71	175	66	0	.	35	71	0	.	0	.	136	57	7	100	34	71	437	33
	dead trees	1181	16	0	.	31	79	58	89	17	100	684	29	1029	20	689	27	630	37	405	36	347	40	860	27	441	31	1243	24	7616	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
801-1000 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	13102	11	1944	30	1908	37	2494	30	1469	39	2175	30	9487	14	8711	15	2190	30	1734	37	1343	31	2935	25	2691	25	6295	18	58477	5
	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	439	57	832	35	377	57	51	74	380	37	3398	18
	various	379	43	29	80	17	100	46	87	14	71	50	53	242	30	220	34	6	100	24	74	93	76	0	.	98	39	412	43	1631	17
	indeterminable	1774	16	249	45	138	47	145	46	161	40	300	35	826	22	1414	22	392	34	205	42	335	33	453	33	563	28	2072	21	9028	8
	wood harvest	417	28	66	78	16	70	102	57	66	72	113	69	335	56	214	46	0	.	52	71	88	59	58	100	42	72	0	.	1570	18
	other human activity	263	46	45	67	0	.	21	58	0	.	50	100	351	45	337	46	0	.	15	100	26	78	0	.	125	58	26	100	1259	21
	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	191	39	1079	19
	lying living trees	34	71	0	.	0	.	0	.	17	100	18	100	36	72	15	100	55	74	18	100	39	71	69	61	53	74	133	43	488	22
	dead trees	1055	19	174	43	107	43	245	51	106	57	163	50	1409	23	760	26	475	32	470	39	567	34	725	34	447	35	1335	23	8038	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	11659	12	2790	23	5342	19	9859	13	9689	13	895	47	6335	16	4516	21	1641	30	566	54	1318	39	1180	44	2188	23	5546	19	63524	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	283	43	1938	18
	various	216	31	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	1952	19
	indeterminable	1587	17	352	30	652	26	1077	17	828	19	95	61	820	21	733	27	179	46	54	70	63	50	200	50	557	29	1703	22	8901	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	41	71	0	.	69	47	100	40	0	.	171	46	71	79	0	.	15	100	0	.	25	76	18	100	27	78	632	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	476	36	339	37	1699	22	7867	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
=600 m	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5833	15	8626	13	5401	17	11374	11	21999	8	0	.	1137	52	1557	32	923	56	938	52	169	70	1560	37	111	100	7594	16	67222	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	82	46	150	37	233	27	0	.	0	.	51	61	35	84	23	75	0	.	0	.	0	.	254	49	1109	16
	indeterminable	602	23	1214	18	526	27	922	17	2310	12	0	.	18	71	480	39	127	60	100	66	12	70	196	48	0	.	3105	20	9613	8
	wood harvest	327	44	236	30	138	39	584	22	676	22	0	.	0	.	38	100	0	.	24	100	18	100	0	.	0	.	0	.	2041	13
	other human activity	67	48	51	75	55	52	189	45	394	46	0	.	0	.	84	71	129	87	8	100	0	.	8	100	0	.	212	45	1196	22
	weather	160	41	56	77	226	55	243	27	129	39	0	.	8	100	0	.	0	.	0	.	0	.	27	76	0	.	263	36	1113	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	782	26	565	20	1096	16	0	.	43	60	189	40	60	64	119	72	46	74	217	44	0	.	2820	17	7573	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	52363	4	13360	10	14205	11	23942	8	33345	6	15048	12	35699	7	26363	8	18926	10	10204	14	9744	14	36393	7	45666	6	49248	5	384504	2
	fire	0	.	17	100	0	.	7	100	17	100	0	.	0	.	24	100	7	100	0	.	18	100	43	61	0	.	1105	40	1239	36
	rockfall	1993	25	399	49	0	.	54	100	34	100	515	47	1522	26	986	49	1474	22	1243	28	1590	25	2026	19	1412	20	3328	14	16577	7
	various	1281	17	257	24	269	29	474	36	401	19	358	30	674	18	713	19	462	21	130	43	373	34	839	26	1422	15	2033	19	9686	6
	indeterminable	6051	7	1816	14	1449	16	2144	11	3346	9	2220	14	3966	9	3875	11	3272	11	2079	19	1312	16	5552	8	8420	8	11343	7	56844	3
	wood harvest	1743	15	522	24	435	23	1113	16	1249	15	364	32	1357	21	569	26	159	40	131	45	176	40	281	40	619	27	41	61	8758	6
	other human activity	778	21	136	41	55	52	279	33	494	38	570	40	764	25	892	23	272	45	216	58	112	54	414	34	711	24	445	27	6138	9
	weather	610	20	56	77	370	37	464	26	230	28	289	31	1272	16	464	29	725	23	355	38	357	36	1638	13	1241	16	2467	12	10536	6
	lying living trees	124	43	24	77	24	77	17	100	171	38	68	50	314	41	158	36	134	41	123	48	154	39	856	25	701	35	615	24	3483	12
	dead trees	5224	8	1161	19	1482	18	1612	15	2187	12	3117	16	5836	12	3668	14	4688	13	1677	18	1945	17	7915	10	7181	9	9449	7	57142	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. 1426633. Birmensdorf, Swiss Federal Research Institute WSL

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