Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI3
total number of stems
degree of damage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		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
degree of damage	main tree species	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	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	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
no damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14840	7	2355	21	4672	14	7310	10	12463	9	7747	11	14088	8	12047	8	8965	9	4109	15	4290	15	13943	8	26060	6	8224	8	141112	2
	fir	11948	6	1049	22	1626	18	4611	10	3267	13	2671	16	7119	8	3874	11	1206	19	1124	22	721	25	1346	19	716	20	1439	20	42718	3
	pine	1474	18	559	23	354	30	435	37	1104	14	0	.	733	38	127	41	344	43	286	39	255	58	4217	15	4369	13	329	43	14587	7
	larch	60	48	118	41	332	34	191	38	455	28	17	100	70	66	122	52	269	32	4	100	298	38	8548	9	4850	10	5752	9	21086	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	79	100	20	100	23	81	34	100	0	.	1332	18	2146	16	29	74	3663	12
	other conifers	352	38	207	45	103	51	313	28	602	38	0	.	35	67	62	52	10	100	51	60	19	100	31	75	288	37	18	71	2091	15
	beech	14358	5	5398	10	3958	12	6276	9	8203	7	1793	19	6375	10	5778	10	2551	25	950	21	1899	19	1541	23	1087	18	9520	10	69686	3
	maple	3615	9	936	15	907	24	1133	17	2888	13	932	19	1690	18	1968	14	1682	16	485	22	769	23	754	19	429	31	989	18	19177	4
	ash	1924	14	1318	20	1433	17	1311	21	3400	11	514	26	1036	23	2085	16	686	22	439	28	350	32	756	29	687	31	2237	17	18175	5
	oak	1863	22	708	28	1303	22	452	20	1164	18	77	50	95	43	68	39	351	45	48	43	4	100	811	24	134	52	1814	19	8893	8
	sweet chestnut	0	.	0	.	128	69	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	128	56	7	100	6924	11	7190	11
	other broadleaves	2756	12	1066	24	1371	21	1305	19	2648	12	645	26	996	17	1259	16	1604	18	787	27	814	28	3924	13	2965	18	12318	8	34456	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	53189	3	13715	8	16186	8	23337	6	36194	5	14397	9	32315	5	27408	6	17696	8	8315	11	9419	11	37331	5	43738	5	49593	3	382835	1
slight damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1976	11	403	22	700	19	1196	14	1691	11	1806	17	2393	10	1013	17	1506	11	660	20	691	18	2929	13	5397	7	1339	11	23700	3
	fir	411	16	38	48	22	55	163	26	127	33	190	29	390	24	111	35	74	40	58	46	61	45	161	46	83	41	36	44	1924	9
	pine	84	37	4	100	12	75	23	61	34	41	0	.	50	53	0	.	0	.	10	100	0	.	270	32	144	28	12	58	642	17
	larch	0	.	0	.	0	.	4	100	13	76	0	.	0	.	14	100	10	100	0	.	23	62	458	18	169	26	400	17	1091	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	197	26	163	24	4	100	368	18
	other conifers	10	100	0	.	10	100	29	58	83	48	0	.	0	.	10	100	10	100	0	.	0	.	0	.	21	100	0	.	173	30
	beech	883	15	269	23	291	23	244	24	425	20	173	56	508	33	364	22	125	30	71	39	146	37	164	42	108	32	887	16	4657	7
	maple	183	24	53	44	43	59	49	52	122	34	74	39	72	44	124	35	125	27	64	52	88	41	103	41	69	62	160	38	1329	11
	ash	78	32	8	71	38	50	39	40	26	59	28	54	95	72	120	37	0	.	21	100	65	53	58	41	19	71	153	31	749	15
	oak	44	47	40	63	137	60	36	61	39	48	7	100	12	100	5	100	57	53	9	100	0	.	26	59	0	.	193	31	604	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	71	0	.	652	17	661	17
	other broadleaves	179	24	168	36	24	61	67	61	240	28	117	35	198	37	150	34	138	34	123	42	106	59	258	26	393	24	887	13	3047	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3848	7	983	14	1276	15	1851	11	2800	8	2394	14	3723	9	1912	11	2044	10	1016	16	1181	14	4633	9	6565	6	4720	6	38945	2
moderate damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1021	11	64	41	196	23	415	18	681	14	736	17	1250	11	796	17	802	14	331	22	441	22	1041	16	1818	9	702	15	10292	4
	fir	692	12	39	42	118	38	327	22	202	20	187	30	477	15	207	24	72	33	119	34	42	41	119	42	69	35	75	40	2746	7
	pine	96	37	16	61	11	73	10	100	74	30	0	.	39	61	4	100	8	71	65	62	12	100	195	26	277	26	44	65	850	13
	larch	0	.	0	.	4	100	4	100	14	77	0	.	0	.	11	100	36	72	0	.	4	100	411	17	233	22	208	22	925	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	107	30	96	30	0	.	207	21
	other conifers	98	55	0	.	0	.	5	100	30	75	0	.	9	100	10	100	10	100	0	.	9	100	0	.	31	58	0	.	202	32
	beech	1744	12	416	17	322	21	419	20	534	16	241	27	847	14	537	18	378	26	133	34	461	26	263	34	259	27	954	15	7508	5
	maple	245	21	74	40	46	52	63	50	135	32	122	32	90	34	263	32	159	26	29	63	147	29	158	31	8	70	133	38	1673	9
	ash	134	34	104	37	124	33	33	53	86	33	39	63	110	40	87	44	60	58	70	54	173	67	74	63	35	60	167	29	1295	14
	oak	28	55	22	61	22	61	55	46	52	38	10	100	11	100	10	100	10	100	4	100	0	.	64	49	4	100	172	30	464	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	9	100	852	14	870	14
	other broadleaves	211	22	165	31	52	42	57	46	168	27	91	52	113	33	118	35	199	24	125	54	136	53	469	19	382	22	1438	11	3723	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4271	7	901	14	895	13	1387	10	1974	8	1426	13	2951	8	2044	11	1732	12	875	17	1425	20	2911	10	3222	7	4742	6	30756	2
severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1023	11	81	29	337	20	398	17	546	14	651	15	1307	12	978	19	1043	12	385	21	401	18	935	12	1636	10	598	15	10320	4
	fir	896	13	74	34	50	43	254	20	205	21	159	26	750	13	358	18	131	30	92	38	41	59	117	29	59	37	75	31	3260	6
	pine	79	44	54	86	27	47	16	50	117	25	0	.	39	59	0	.	20	71	97	57	0	.	255	28	328	22	40	49	1071	13
	larch	10	100	0	.	4	100	0	.	8	71	0	.	4	100	0	.	20	71	0	.	32	52	401	16	381	20	518	20	1379	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	15	100	4	100	4	100	11	100	0	.	156	26	226	24	0	.	416	17
	other conifers	40	50	29	58	0	.	17	80	23	61	0	.	0	.	23	71	0	.	0	.	10	100	0	.	81	56	0	.	223	26
	beech	1870	10	415	16	298	25	370	18	604	14	119	33	722	20	511	19	430	24	242	38	336	30	79	51	161	30	811	15	6968	5
	maple	363	17	116	34	69	43	62	41	233	24	99	39	183	31	187	38	165	31	48	67	92	35	100	42	65	46	124	31	1906	9
	ash	280	33	111	41	110	28	109	29	126	31	63	38	115	36	147	35	62	44	29	75	118	38	57	49	68	51	154	31	1551	10
	oak	82	39	46	43	24	47	27	61	116	37	12	100	19	80	10	100	33	66	0	.	0	.	40	62	9	71	232	30	649	15
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	86	0	.	1878	13	1925	13
	other broadleaves	380	23	104	29	57	40	102	38	259	24	102	40	211	24	339	27	247	26	103	46	50	41	507	21	575	21	1357	11	4394	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5023	6	1031	12	983	13	1356	10	2236	8	1204	11	3367	8	2558	12	2154	10	1007	16	1080	15	2687	8	3589	7	5787	6	34062	2
very severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	169	24	27	54	43	44	62	35	77	32	164	21	267	27	80	34	214	22	66	32	134	28	180	21	398	18	135	26	2017	7
	fir	217	20	23	61	4	100	42	44	10	100	86	34	108	27	95	36	32	77	29	58	19	65	40	47	33	50	48	49	788	11
	pine	36	45	4	100	11	58	4	100	12	58	0	.	0	.	4	100	0	.	11	100	10	100	153	34	176	40	0	.	421	21
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	147	24	59	34	223	23	442	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	99	38	85	33	0	.	189	25
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	183	68	61	58	10	100	59	70	368	40	0	.	692	29
	beech	488	14	102	27	72	29	134	25	196	27	49	39	275	20	112	26	103	41	55	41	209	39	42	56	140	35	326	22	2303	8
	maple	150	28	34	53	22	71	20	71	75	34	84	42	70	39	38	58	32	52	0	.	58	43	34	58	37	60	28	58	684	13
	ash	34	44	19	71	42	50	16	74	48	52	10	100	32	58	37	52	43	60	14	78	128	64	56	66	35	59	61	53	576	20
	oak	36	80	12	58	55	62	18	64	50	51	0	.	4	100	0	.	0	.	0	.	0	.	40	50	0	.	64	41	279	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	840	16	840	16
	other broadleaves	163	25	46	47	49	53	40	44	148	29	72	43	124	46	65	39	148	35	42	50	111	53	463	27	205	28	755	17	2430	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1294	9	267	19	298	20	335	16	617	18	466	17	880	14	443	16	756	21	283	23	692	23	1313	13	1537	14	2480	9	11661	4
dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1376	11	225	23	516	22	497	24	1010	18	2123	15	2649	13	1862	17	2215	16	628	24	1081	19	2734	12	3412	10	1222	20	21549	4
	fir	1083	13	107	39	48	52	322	22	233	28	314	21	781	16	327	23	162	29	184	34	84	37	281	33	179	36	141	35	4248	7
	pine	221	24	146	46	77	50	33	47	136	32	21	100	166	47	38	63	119	53	97	41	26	100	1095	19	1149	26	55	57	3380	12
	larch	59	74	0	.	25	62	20	71	0	.	0	.	0	.	0	.	32	68	0	.	54	69	1087	16	596	20	790	16	2664	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	74	0	.	0	.	83	47	233	30	0	.	327	25
	other conifers	48	81	0	.	10	100	0	.	10	100	0	.	10	100	0	.	10	100	10	100	0	.	0	.	151	43	0	.	249	31
	beech	536	18	269	29	235	29	183	24	344	20	73	38	384	23	512	22	163	31	76	51	184	30	111	53	109	36	420	23	3599	7
	maple	123	33	64	39	62	45	10	100	112	33	57	63	21	71	89	38	57	41	9	100	90	45	64	52	20	100	35	53	814	13
	ash	106	40	9	100	60	41	10	100	145	32	59	41	113	47	108	36	54	49	19	71	69	83	50	66	44	73	111	39	958	14
	oak	194	29	109	46	162	49	45	46	53	50	15	77	0	.	10	100	30	58	10	100	0	.	34	52	40	79	161	42	863	16
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	67	0	.	2977	13	3068	13
	other broadleaves	386	19	117	32	269	34	109	32	279	22	346	25	444	28	449	27	554	24	139	32	231	42	815	18	806	20	2427	11	7371	6
	indeterminable	50	45	10	100	38	50	0	.	19	71	82	40	84	35	59	41	311	30	20	71	31	53	256	23	355	22	204	26	1518	10
	total	4183	7	1056	15	1519	14	1229	14	2341	10	3091	12	4653	10	3454	11	3719	12	1192	17	1849	15	6684	8	7094	7	8543	7	50607	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20405	6	3154	18	6463	12	9879	9	16468	8	13226	10	21955	7	16776	8	14745	8	6178	13	7038	13	21762	7	38722	5	12219	8	208990	2
	fir	15248	5	1331	20	1868	18	5719	10	4044	11	3608	15	9626	8	4971	10	1676	17	1606	20	968	23	2065	19	1139	17	1814	18	55684	3
	pine	1990	16	783	23	492	28	521	32	1477	13	21	100	1026	35	173	38	491	36	566	32	303	58	6185	14	6443	13	480	44	20951	7
	larch	129	62	118	41	365	33	219	38	490	27	17	100	74	64	147	52	368	29	4	100	424	34	11053	8	6289	9	7890	9	27586	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	102	100	24	100	39	58	49	100	0	.	1973	16	2949	15	33	66	5170	11
	other conifers	547	37	236	40	122	53	364	26	748	33	0	.	55	50	115	39	224	60	122	56	48	65	90	53	941	31	18	71	3630	13
	beech	19879	5	6870	9	5177	11	7625	9	10306	7	2448	19	9111	9	7815	10	3749	19	1527	20	3235	16	2200	21	1863	17	12918	9	94722	3
	maple	4680	8	1276	14	1149	23	1337	16	3565	12	1368	17	2126	17	2670	14	2221	15	635	23	1244	20	1213	18	628	25	1469	18	25584	4
	ash	2557	13	1570	19	1807	16	1519	19	3830	11	714	24	1501	23	2585	15	905	22	592	28	903	41	1052	24	888	30	2883	15	23304	5
	oak	2247	20	938	27	1703	21	633	18	1474	17	121	45	141	36	104	43	480	44	71	45	4	100	1015	23	186	56	2634	17	11751	8
	sweet chestnut	0	.	0	.	153	72	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	260	59	17	100	14122	9	14555	9
	other broadleaves	4075	10	1666	20	1820	19	1680	18	3741	11	1373	20	2087	16	2381	17	2889	15	1318	28	1447	27	6435	12	5326	16	19181	7	55420	4
	indeterminable	50	45	10	100	38	50	0	.	19	71	82	40	84	35	59	41	311	30	20	71	31	53	256	23	355	22	204	26	1518	10
	total	71808	3	17952	8	21158	7	29496	6	46162	4	22978	8	47887	5	37819	6	28102	7	12688	10	15646	10	55559	5	65745	4	75866	3	548866	1

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. 1259111. Birmensdorf, Swiss Federal Research Institute WSL

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