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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NFI5
total number of stems
degree of damage · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	9654	9	1818	27	2646	23	7420	14	7619	12	5495	15	13255	11	8613	12	8018	13	3467	20	3305	22	12115	11	20420	8	7212	15	111057	3
	fir	12597	8	934	31	1443	23	5741	13	2709	16	2493	23	8386	12	3046	16	1277	23	1397	27	410	29	979	27	357	28	1216	26	42984	4
	pine	839	28	348	31	207	39	418	37	523	25	0	.	762	63	30	64	118	71	299	50	0	.	3877	24	3188	21	161	66	10770	12
	larch	7	100	120	53	671	64	92	48	619	37	118	73	50	76	135	74	384	43	0	.	111	70	5542	12	2977	13	4379	15	15204	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	856	26	1865	28	49	100	2779	20
	other conifers	246	43	200	57	28	100	276	29	460	32	0	.	22	75	95	61	17	100	52	100	18	100	0	.	163	58	44	70	1620	16
	beech	12446	7	4830	13	3508	19	5343	11	7173	10	1778	25	5671	12	4544	14	2212	23	1121	30	1642	27	1569	29	915	25	8501	14	61252	4
	maple	4357	13	1292	20	958	34	1167	20	2792	14	870	24	1547	18	1805	20	1917	20	388	32	563	29	803	31	417	41	698	32	19574	6
	ash	1060	21	780	25	727	26	803	23	1252	19	615	36	484	31	579	25	530	31	333	42	44	55	535	32	345	58	1547	24	9635	8
	oak	1691	33	480	30	894	33	423	27	669	19	88	56	36	100	16	71	78	72	60	66	0	.	886	38	84	74	1481	25	6886	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	81	18	100	4230	17	4318	17
	other broadleaves	2257	15	831	22	994	27	1859	23	2930	16	659	28	1032	21	796	27	1210	23	447	30	432	44	2706	18	2446	26	9739	13	28339	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	45154	4	11632	11	12076	12	23540	8	26746	6	12117	12	31246	7	19659	9	15771	10	7564	16	6524	16	29937	7	33196	7	39257	6	314419	2
slight damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1609	13	370	29	534	28	1129	23	1718	16	1534	20	2066	13	1265	16	1782	15	1055	22	787	24	3507	11	6691	9	2082	14	26127	4
	fir	572	17	31	64	58	49	353	24	159	36	167	43	501	22	131	38	229	32	193	39	45	53	222	32	89	41	86	52	2837	9
	pine	89	56	40	54	0	.	14	71	14	71	0	.	40	63	0	.	41	71	17	100	0	.	215	39	162	43	23	76	655	20
	larch	7	100	7	100	0	.	0	.	7	100	22	100	17	100	0	.	43	72	0	.	6	100	621	22	375	23	186	32	1292	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	457	43	345	27	0	.	802	27
	other conifers	17	100	34	71	20	100	24	77	55	52	0	.	35	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	185	30
	beech	1439	16	365	22	203	47	355	22	475	20	175	61	655	21	515	20	298	31	124	50	128	33	132	85	156	43	1018	16	6038	7
	maple	344	22	94	42	43	61	77	57	227	29	105	48	218	30	212	30	234	37	0	.	118	37	59	53	80	60	213	35	2027	10
	ash	85	47	7	100	27	50	81	56	72	40	15	100	7	100	35	70	68	60	15	100	48	53	60	89	28	74	109	50	658	18
	oak	48	54	0	.	106	50	0	.	17	100	0	.	0	.	29	78	44	60	0	.	0	.	53	74	7	100	75	53	378	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	925	38	925	38
	other broadleaves	250	33	7	100	100	42	88	45	193	31	61	50	211	36	154	48	225	30	53	58	75	52	433	27	338	34	928	20	3115	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4461	8	955	16	1090	20	2121	15	2936	11	2079	17	3751	10	2340	11	2965	12	1459	19	1207	18	5759	10	8270	8	5647	9	45040	3
moderate damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	877	19	138	32	258	28	202	31	549	17	503	22	967	15	916	18	711	24	388	28	386	25	1177	15	1747	13	945	21	9764	5
	fir	778	16	31	64	33	45	203	28	224	26	251	39	561	22	298	27	167	33	180	35	62	42	144	44	45	54	111	38	3089	8
	pine	121	50	89	65	13	71	20	58	64	41	0	.	45	63	7	100	0	.	18	100	0	.	175	31	397	29	32	100	982	16
	larch	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	18	100	0	.	0	.	230	23	124	37	306	23	692	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	41	169	33	0	.	228	26
	other conifers	52	57	42	61	0	.	50	55	86	83	0	.	0	.	0	.	0	.	0	.	46	100	0	.	17	100	0	.	293	34
	beech	1809	13	538	22	192	31	560	21	566	17	108	43	1238	18	602	20	385	41	346	32	317	38	176	39	249	35	1251	17	8338	6
	maple	382	22	155	48	34	71	85	45	188	28	94	94	254	41	291	32	278	28	120	51	178	34	128	43	56	52	175	35	2417	10
	ash	117	44	41	61	75	45	67	48	84	43	30	78	53	63	127	47	15	100	143	46	52	52	85	55	75	61	138	36	1103	14
	oak	78	56	16	71	45	49	13	71	38	67	0	.	22	100	0	.	18	100	0	.	0	.	53	58	0	.	54	71	337	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1143	20	1143	20
	other broadleaves	171	35	82	52	103	46	160	32	225	26	26	74	90	42	161	43	206	37	22	75	129	38	404	26	329	32	1349	17	3458	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4392	8	1132	19	755	18	1361	13	2032	10	1011	19	3230	10	2401	13	1797	15	1218	20	1171	17	2632	11	3208	9	5505	8	31845	3
severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	784	18	14	70	156	36	166	31	263	22	399	22	944	19	452	20	506	20	271	40	243	28	655	18	1169	14	644	24	6667	6
	fir	592	18	24	77	35	52	249	23	108	31	83	43	451	20	202	29	139	34	117	48	43	54	120	42	117	49	128	30	2408	8
	pine	30	50	7	100	33	66	21	74	20	58	0	.	0	.	0	.	17	100	18	100	0	.	244	37	266	28	6	100	663	18
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	277	25	224	30	329	34	855	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	40	156	40	0	.	289	28
	other conifers	86	60	17	100	0	.	0	.	51	58	0	.	0	.	36	71	0	.	0	.	0	.	17	100	69	61	0	.	276	29
	beech	1121	17	335	25	172	31	235	27	461	23	56	47	616	22	365	22	128	33	85	50	92	52	225	48	204	40	849	20	4944	7
	maple	196	30	113	42	33	66	31	64	190	28	82	50	161	43	161	41	158	38	37	61	151	42	152	46	57	53	16	100	1538	12
	ash	473	32	205	37	406	38	136	31	468	23	150	53	469	55	306	28	131	39	146	69	74	50	94	53	116	50	320	54	3494	13
	oak	82	57	31	64	80	67	72	44	43	49	0	.	17	100	0	.	36	71	7	100	0	.	53	75	0	.	240	52	662	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	12	100	2589	17	2614	17
	other broadleaves	217	29	83	41	111	66	112	47	281	25	33	71	117	44	130	38	117	38	62	59	77	50	406	38	356	32	1184	15	3283	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3579	9	829	17	1026	20	1022	15	1885	11	802	19	2774	14	1651	12	1232	14	743	26	705	19	2389	12	2748	10	6307	9	27693	3
very severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	219	27	34	71	20	100	66	38	67	44	114	43	252	35	213	28	276	28	103	80	172	35	257	26	478	23	130	36	2400	10
	fir	169	29	0	.	35	53	70	40	41	54	54	49	107	35	37	64	14	71	35	45	20	73	37	60	26	77	22	58	665	14
	pine	81	40	14	71	7	100	24	100	34	45	0	.	36	100	0	.	0	.	8	100	0	.	110	54	156	40	0	.	470	22
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	272	27	66	45	101	32	439	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	50	47	49	0	.	132	37
	other conifers	17	100	0	.	0	.	0	.	34	71	0	.	0	.	39	71	0	.	35	100	18	100	156	62	268	58	0	.	567	34
	beech	507	19	226	32	40	61	163	28	353	22	38	80	399	25	188	29	91	50	58	56	178	41	114	44	92	56	471	23	2918	8
	maple	138	32	16	71	20	100	31	63	129	33	83	79	96	47	36	100	79	55	0	.	103	42	63	53	53	55	19	100	864	16
	ash	91	42	101	57	48	55	68	48	221	28	22	100	117	41	265	34	33	71	82	64	139	44	137	56	25	79	126	43	1474	13
	oak	49	71	17	100	16	72	14	71	64	69	0	.	6	100	0	.	0	.	0	.	0	.	35	71	0	.	127	59	329	30
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	15	100	1419	17	1449	17
	other broadleaves	143	41	100	47	24	77	31	71	113	46	54	58	108	47	122	41	51	54	18	100	96	51	331	41	235	38	761	24	2188	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1414	12	509	23	209	27	466	19	1056	14	364	27	1122	18	899	16	552	19	339	33	726	21	1605	17	1459	17	3177	11	13896	5
dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2138	14	407	31	416	33	609	23	809	22	2121	19	3418	17	2685	20	2809	18	723	26	846	25	3644	15	4362	12	1420	18	26408	5
	fir	1418	16	170	40	85	43	379	23	276	26	270	31	841	18	562	26	242	35	328	38	125	45	161	35	183	68	163	33	5205	8
	pine	441	28	119	59	41	85	61	54	129	42	0	.	165	68	0	.	137	54	43	71	0	.	1416	21	1082	28	105	53	3739	13
	larch	0	.	0	.	34	64	0	.	17	100	44	100	17	100	49	100	97	57	0	.	61	77	1365	20	787	22	720	22	3192	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	187	38	82	53	0	.	268	31
	other conifers	37	71	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	35	71	283	49	0	.	406	36
	beech	1384	17	554	24	349	36	298	29	318	23	178	35	745	25	712	22	251	30	72	55	607	46	248	48	176	38	699	25	6593	8
	maple	306	34	120	39	20	100	36	71	168	52	18	100	91	45	103	69	113	50	51	58	127	59	127	52	0	.	76	80	1354	16
	ash	518	32	316	36	212	47	129	41	547	31	104	58	529	31	381	34	171	49	49	58	125	54	77	46	104	63	180	45	3441	11
	oak	320	33	90	60	213	42	71	50	24	77	18	100	22	75	54	100	18	100	18	100	0	.	70	50	0	.	434	34	1352	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	5022	14	5059	14
	other broadleaves	640	23	224	33	285	37	85	53	339	26	394	39	379	30	495	40	592	28	253	61	394	35	1082	21	933	25	2731	14	8826	8
	indeterminable	237	33	7	100	0	.	17	100	67	50	88	44	170	30	83	43	175	34	14	100	14	100	126	37	230	32	77	50	1305	12
	total	7437	8	2007	16	1656	18	1702	14	2693	12	3234	15	6379	11	5125	13	4605	13	1587	20	2299	19	8577	10	8222	10	11627	8	67148	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15280	8	2781	22	4030	20	9593	13	11025	11	10165	14	20902	10	14143	11	14102	11	6007	18	5739	18	21354	10	34866	7	12434	13	182423	3
	fir	16126	8	1190	28	1690	22	6995	12	3517	14	3318	21	10848	11	4276	15	2068	21	2251	24	704	27	1663	24	818	26	1726	24	57188	4
	pine	1601	23	618	26	302	40	557	33	783	21	0	.	1049	52	37	55	314	57	404	50	0	.	6037	19	5251	20	328	58	17280	10
	larch	21	58	127	51	705	64	92	48	650	36	184	73	85	84	184	81	543	37	0	.	204	65	8307	12	4553	12	6022	14	21675	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1778	24	2663	24	49	100	4500	17
	other conifers	455	39	293	49	47	72	367	28	686	37	0	.	57	52	170	40	17	100	121	100	82	64	208	56	801	42	44	70	3349	16
	beech	18708	7	6848	12	4464	17	6954	10	9345	9	2332	24	9326	11	6926	13	3364	20	1807	26	2963	23	2464	27	1792	24	12790	13	90082	3
	maple	5722	12	1790	19	1108	34	1426	19	3694	13	1252	23	2366	18	2608	18	2780	18	595	30	1240	26	1332	26	663	31	1197	27	27773	5
	ash	2344	18	1449	22	1496	24	1283	19	2644	15	936	30	1658	26	1693	20	949	29	769	35	482	34	989	27	693	49	2420	23	19805	6
	oak	2268	30	634	29	1354	30	593	23	855	18	106	50	103	49	98	66	194	68	85	57	0	.	1150	33	91	72	2412	26	9943	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	127	74	45	72	15330	12	15510	12
	other broadleaves	3677	13	1327	18	1616	22	2335	20	4082	14	1226	24	1938	18	1858	25	2399	20	855	30	1203	31	5363	15	4637	23	16692	11	49210	5
	indeterminable	237	33	7	100	0	.	17	100	67	50	88	44	170	30	83	43	175	34	14	100	14	100	126	37	230	32	77	50	1305	12
	total	66437	4	17064	10	16813	11	30212	7	37348	6	19606	11	48502	6	32075	8	26922	9	12909	14	12631	14	50898	7	57103	6	71520	5	500040	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. 1259276. Birmensdorf, Swiss Federal Research Institute WSL

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