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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI4
total number of stems with two most important forms of damages
slope (in 20% 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
slope (in 20% 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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
to 20%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	15822	10	4507	18	10125	13	14889	11	20617	8	2529	30	5683	20	3371	24	739	63	678	52	229	60	1301	47	3146	25	915	43	84552	4
	fire	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	14	71
	rockfall	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	various	592	29	64	51	209	35	304	48	242	26	24	76	33	62	51	50	35	84	0	.	0	.	0	.	112	66	7	100	1671	16
	indeterminable	1408	17	515	28	943	19	1375	14	1872	13	277	37	418	34	380	41	139	77	128	66	15	100	81	56	503	39	259	45	8312	7
	wood harvest	527	26	220	35	328	28	694	21	612	22	6	100	72	54	22	75	0	.	24	100	0	.	0	.	35	100	0	.	2542	11
	other human activity	232	33	42	54	37	61	212	40	141	37	0	.	96	69	69	72	0	.	8	100	0	.	18	100	70	65	19	74	943	17
	weather	72	46	49	87	137	37	237	28	71	49	15	100	90	48	17	100	0	.	0	.	0	.	0	.	89	66	0	.	777	17
	lying living trees	0	.	0	.	17	100	0	.	70	62	0	.	15	100	0	.	18	100	0	.	0	.	0	.	53	58	0	.	173	35
	dead trees	1050	17	228	35	950	24	648	20	1016	17	283	50	459	36	171	47	41	86	109	71	14	100	106	53	241	45	226	62	5540	8
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
21-40 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	18764	9	3560	19	3199	26	6864	14	9810	13	5119	23	11787	12	9649	14	2655	24	1509	33	454	46	4126	22	8707	15	6579	18	92783	4
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	7	100	32	80
	rockfall	59	89	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	18	100	0	.	44	100	139	53
	various	385	27	49	48	37	69	107	69	123	33	70	43	325	26	191	35	63	44	61	80	76	91	184	71	293	37	82	43	2046	13
	indeterminable	1925	12	433	27	292	38	461	26	859	17	427	29	1032	17	1089	18	310	29	387	40	64	53	607	29	1480	22	1073	22	10439	6
	wood harvest	707	23	260	37	93	44	249	33	530	24	151	47	768	33	222	44	14	71	0	.	20	100	31	100	57	59	18	100	3119	12
	other human activity	203	32	95	55	17	100	67	52	330	54	319	60	286	35	351	44	143	79	0	.	7	100	80	44	73	46	174	42	2146	17
	weather	227	40	0	.	203	61	163	59	44	49	53	51	319	29	54	58	0	.	40	71	0	.	69	48	337	40	104	43	1615	16
	lying living trees	98	50	0	.	7	100	17	100	0	.	50	58	22	100	64	61	37	71	0	.	0	.	17	100	52	58	41	61	405	22
	dead trees	1534	18	198	34	378	35	530	26	584	21	681	36	1930	28	997	39	266	32	217	48	55	74	688	33	1157	23	886	27	10102	9
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
41-60 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	11288	12	4439	21	881	39	1589	29	2518	25	4087	22	8350	15	8538	15	6194	17	1790	36	2907	25	14261	12	15373	10	15379	11	97594	4
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	535	41	535	41
	rockfall	258	55	59	72	0	.	0	.	0	.	0	.	15	72	7	100	210	55	33	71	51	89	118	77	18	100	278	46	1046	24
	various	169	39	96	41	23	77	63	69	30	64	163	56	196	37	235	29	185	36	0	.	177	50	340	43	451	26	969	32	3099	13
	indeterminable	1715	16	685	26	214	46	196	32	475	30	815	21	1186	19	1133	24	1182	19	202	42	378	33	1997	15	2394	14	3852	15	16424	6
	wood harvest	471	36	42	71	14	100	157	43	106	62	207	46	351	30	217	43	58	73	47	81	34	71	118	73	242	47	7	100	2070	15
	other human activity	335	37	0	.	0	.	0	.	23	77	55	74	285	52	305	35	104	42	71	100	24	77	237	53	276	35	69	59	1783	16
	weather	203	33	7	100	31	100	38	69	92	50	73	54	271	40	130	42	260	43	18	100	69	70	502	28	250	31	870	21	2814	11
	lying living trees	0	.	24	77	0	.	0	.	67	61	18	100	175	66	58	58	18	100	0	.	35	71	309	53	96	49	286	43	1085	23
	dead trees	1660	16	447	29	153	46	210	54	450	35	919	25	1309	23	1409	21	1400	23	187	40	463	34	2601	19	2000	17	2676	17	15883	6
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
61-80 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	5379	19	689	42	0	.	600	40	374	59	2129	30	7445	18	2834	26	6529	17	3651	22	3958	24	11162	13	13398	12	14458	11	72606	5
	fire	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	7	100	0	.	0	.	18	100	0	.	375	93	418	83
	rockfall	1107	32	17	100	0	.	54	100	34	100	269	76	472	44	283	68	510	35	811	35	258	37	771	31	572	26	1091	25	6250	12
	various	135	41	31	64	0	.	0	.	7	100	73	58	113	43	87	51	91	37	38	53	96	55	268	34	416	26	641	31	1997	14
	indeterminable	691	27	132	53	0	.	112	57	134	55	337	31	949	22	776	29	1006	21	878	35	564	25	2009	15	2798	13	3153	17	13538	7
	wood harvest	31	71	0	.	0	.	13	100	0	.	0	.	166	60	108	56	54	75	60	63	121	51	133	51	271	42	16	100	973	20
	other human activity	8	100	0	.	0	.	0	.	0	.	196	59	60	51	107	60	7	100	137	74	62	88	72	63	210	56	96	54	955	23
	weather	91	48	0	.	0	.	26	79	7	100	56	80	400	27	112	78	243	34	204	57	250	47	735	20	332	26	834	23	3289	11
	lying living trees	19	100	0	.	0	.	0	.	17	100	0	.	68	54	15	100	55	75	36	71	84	58	311	35	456	52	191	30	1252	22
	dead trees	702	27	136	68	0	.	224	49	106	61	703	41	1686	22	633	29	1631	20	641	30	733	29	2416	19	2562	17	2938	15	15112	7
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
81-100 %	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	792	44	164	75	0	.	0	.	24	.	653	68	1532	30	1777	28	2306	27	2225	34	1010	34	4375	20	3818	20	9101	15	27779	8
	fire	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	188	91	222	77
	rockfall	536	56	323	59	0	.	0	.	0	.	150	71	621	46	113	49	588	38	241	57	772	36	582	29	487	38	1174	24	5586	13
	various	0	.	17	100	0	.	0	.	0	.	7	100	7	100	123	63	57	62	23	75	23	78	28	63	75	51	156	44	516	24
	indeterminable	222	45	51	74	0	.	0	.	7	.	132	73	229	35	383	36	455	32	334	46	153	39	600	27	923	23	2098	20	5586	10
	wood harvest	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	71	0	.	0	.	0	.	15	100	0	.	54	52
	other human activity	0	.	0	.	0	.	0	.	0	.	0	.	13	71	18	100	18	100	0	.	19	100	7	100	81	80	79	91	236	44
	weather	17	100	0	.	0	.	0	.	17	.	84	71	88	66	150	52	145	56	29	100	14	71	285	28	180	46	502	25	1511	15
	lying living trees	7	100	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	18	100	95	52	43	61	32	70	212	31
	dead trees	160	49	153	79	0	.	0	.	31	.	366	50	192	41	363	40	1039	39	310	42	361	41	1597	21	967	24	2128	20	7666	10
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
>100%	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	no visible damage	317	71	0	.	0	.	0	.	0	.	531	70	902	43	194	69	503	45	351	62	1185	51	1169	30	1223	31	2816	24	9190	13
	fire	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	rockfall	26	100	0	.	0	.	0	.	0	.	96	73	414	50	565	79	166	71	158	81	509	51	538	43	335	51	741	30	3549	19
	various	0	.	0	.	0	.	0	.	0	.	21	100	0	.	27	81	31	100	8	100	0	.	18	100	75	65	177	53	357	32
	indeterminable	90	100	0	.	0	.	0	.	0	.	232	60	152	42	114	78	181	52	150	69	138	49	258	36	323	37	907	28	2545	15
	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	.	24	100	42	100	0	.	0	.	0	.	0	.	0	.	9	100	75	66
	weather	0	.	0	.	0	.	0	.	0	.	7	100	104	59	0	.	77	61	63	88	25	76	47	54	51	58	157	41	531	23
	lying living trees	0	.	0	.	0	.	0	.	0	.	0	.	35	100	21	100	7	100	88	60	17	100	123	55	0	.	64	67	355	30
	dead trees	118	71	0	.	0	.	0	.	0	.	166	61	260	53	96	65	311	49	212	56	318	46	509	36	254	44	595	35	2838	15
	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. 1426410. Birmensdorf, Swiss Federal Research Institute WSL

back