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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NFI2
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 1993/95
		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	15427	9	3406	21	5443	16	10755	13	15292	11	9624	14	16927	10	12327	12	8041	13	3925	21	3869	23	11281	12	21528	8	7072	12	144918	3
	fir	10769	9	1121	28	1635	27	5376	13	3101	15	2543	22	6727	11	3328	14	1214	24	1260	26	518	31	800	31	497	30	1000	30	39888	4
	pine	1258	24	959	32	648	41	543	32	1307	17	0	.	679	61	54	48	140	61	304	55	0	.	4417	23	3864	19	169	61	14342	10
	larch	75	72	135	50	200	50	143	64	521	29	0	.	14	100	147	71	255	35	7	100	200	53	5910	13	3311	13	3907	15	14826	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	770	27	1782	22	16	100	2583	17
	other conifers	142	44	205	77	75	76	337	36	368	48	15	100	24	77	18	100	17	100	70	78	0	.	104	85	35	100	8	100	1420	21
	beech	15343	7	5515	13	3534	16	7058	10	8249	9	1950	27	6750	13	5804	14	2546	23	1027	27	1680	23	1651	32	1232	29	8911	15	71250	4
	maple	3019	12	930	20	264	38	578	24	2383	16	732	25	1309	18	1735	19	1336	26	337	37	717	32	517	28	260	44	442	30	14559	6
	ash	2201	20	875	30	1391	24	1267	29	2630	14	820	34	1022	28	1963	20	371	37	357	41	388	53	532	30	519	70	1064	26	15398	7
	oak	2031	29	689	31	2296	31	706	25	1023	20	125	48	83	64	85	61	161	55	43	62	0	.	461	29	84	74	1890	27	9677	12
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	57	28	100	7047	14	7204	14
	other broadleaves	2427	16	817	24	1228	27	1246	30	2221	16	748	25	991	27	1736	24	1462	21	541	40	724	33	2251	20	1941	24	9915	12	28250	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	52691	4	14653	10	16733	10	28009	8	37095	6	16557	12	34526	7	27198	8	15559	10	7871	15	8094	16	28806	8	35080	6	41442	5	364314	2
slight damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1414	18	151	31	665	24	810	17	1172	22	1333	18	1399	13	1208	18	1591	18	588	25	387	28	2461	14	2421	11	719	18	16321	5
	fir	615	18	52	58	55	49	513	23	130	34	125	48	473	22	195	30	47	48	68	62	48	65	26	77	21	100	115	48	2482	9
	pine	117	48	7	100	15	71	7	100	31	64	0	.	11	100	0	.	0	.	37	71	0	.	339	36	119	43	0	.	681	22
	larch	0	.	0	.	0	.	0	.	13	71	0	.	0	.	0	.	14	100	0	.	35	100	555	30	145	27	103	34	866	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	109	37	258	30	0	.	367	24
	other conifers	51	74	0	.	69	100	0	.	33	71	0	.	0	.	18	100	0	.	0	.	15	100	0	.	0	.	0	.	187	46
	beech	1030	17	132	38	206	33	342	25	430	24	206	92	693	38	297	28	119	40	238	33	152	37	127	67	140	40	502	26	4614	10
	maple	164	33	70	50	0	.	19	100	66	47	98	48	147	41	72	42	89	44	34	71	85	45	31	70	39	70	36	100	951	14
	ash	110	43	34	82	73	56	29	50	182	33	15	100	53	58	175	51	71	50	15	100	26	100	25	79	0	.	121	39	929	16
	oak	89	52	24	77	48	62	7	100	39	69	22	100	118	100	12	100	0	.	0	.	0	.	0	.	0	.	53	54	411	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	780	18	806	18
	other broadleaves	264	41	84	45	56	58	102	55	68	50	91	57	206	51	87	60	112	53	75	60	68	50	608	37	296	38	632	20	2747	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3854	10	555	21	1185	18	1829	12	2165	14	1890	20	3100	14	2064	13	2043	15	1053	19	816	19	4306	11	3439	10	3060	9	31360	3
moderate damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1549	16	186	41	276	25	625	18	854	17	1047	24	1491	13	1395	21	1054	21	497	28	637	30	1262	14	2111	14	672	27	13657	5
	fir	1092	14	28	50	83	50	434	21	207	28	134	47	1366	17	240	24	220	37	189	37	86	54	158	39	92	38	53	54	4381	8
	pine	115	38	45	49	17	100	14	71	58	50	0	.	21	100	8	100	0	.	71	61	0	.	263	26	307	32	38	61	957	15
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	79	352	22	351	27	347	30	1075	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	39	106	34	0	.	268	27
	other conifers	17	100	17	100	0	.	24	77	17	100	0	.	0	.	0	.	0	.	0	.	0	.	35	100	17	100	0	.	127	41
	beech	2814	12	838	23	226	29	686	19	639	20	255	38	921	20	813	26	273	27	287	36	379	27	160	46	201	33	702	19	9195	6
	maple	334	22	183	47	61	52	36	71	192	29	112	46	221	35	267	37	146	56	46	57	243	29	177	52	45	62	54	58	2116	11
	ash	143	50	76	56	33	71	86	42	136	41	15	100	134	47	149	39	54	58	31	70	127	77	52	58	69	60	92	54	1198	16
	oak	70	50	7	100	39	55	68	59	38	55	0	.	6	100	18	100	18	100	7	100	0	.	17	100	0	.	55	58	343	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	731	21	750	21
	other broadleaves	244	30	188	39	17	100	235	39	257	29	70	51	130	38	192	45	32	63	98	68	130	62	453	27	352	29	1014	15	3413	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6378	8	1568	19	752	16	2208	11	2397	10	1632	18	4290	10	3081	15	1797	16	1226	20	1627	18	3110	10	3652	10	3758	8	37479	3
severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1272	14	79	35	295	25	550	19	596	16	1033	17	1263	15	1241	26	765	17	320	26	599	26	1223	14	1890	11	509	21	11634	5
	fir	816	15	21	57	88	44	498	19	207	25	204	41	731	17	162	29	119	37	136	45	41	48	191	34	133	37	68	45	3416	7
	pine	152	40	14	71	7	100	17	71	105	45	0	.	29	72	9	100	17	100	18	100	0	.	466	26	254	37	65	61	1154	16
	larch	7	100	17	100	0	.	7	100	0	.	0	.	9	100	0	.	24	77	0	.	18	100	671	20	314	28	242	27	1308	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	240	35	150	31	0	.	390	25
	other conifers	0	.	34	71	0	.	0	.	0	.	0	.	21	100	21	100	0	.	0	.	15	100	35	100	0	.	0	.	126	43
	beech	1315	14	459	21	131	36	457	22	455	18	44	54	874	25	404	23	266	31	131	40	233	44	69	70	306	29	369	25	5513	7
	maple	240	26	61	52	35	71	64	45	133	39	61	86	162	55	104	40	80	42	39	58	198	42	55	58	69	50	0	.	1300	14
	ash	162	34	68	60	167	49	47	59	192	29	8	100	82	58	79	56	32	65	52	58	139	46	85	54	0	.	38	62	1150	14
	oak	65	44	0	.	27	77	62	52	87	49	0	.	0	.	0	.	0	.	8	100	0	.	124	65	0	.	38	71	411	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	2156	17	2175	17
	other broadleaves	269	28	59	52	41	72	119	43	152	36	65	63	190	47	136	39	149	50	107	54	80	53	282	31	391	32	929	21	2968	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4296	7	813	18	792	18	1821	11	1925	11	1415	16	3360	11	2154	17	1453	14	811	19	1323	18	3459	10	3508	9	4414	10	31544	3
very severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	241	25	38	55	92	43	93	40	68	42	242	40	202	29	224	28	296	29	107	40	148	34	529	20	537	19	118	38	2935	8
	fir	159	28	17	100	0	.	70	50	77	41	48	60	192	32	82	40	44	51	82	75	68	55	48	54	21	100	25	59	933	14
	pine	72	44	0	.	0	.	58	76	7	100	0	.	23	100	9	100	0	.	0	.	0	.	134	41	121	51	32	100	456	24
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	38	55	45	154	41	316	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	109	50	39	68	0	.	148	41
	other conifers	0	.	17	100	0	.	7	100	0	.	0	.	0	.	18	100	0	.	0	.	34	71	104	71	143	59	0	.	322	36
	beech	457	23	80	41	76	49	83	43	197	42	31	65	321	27	210	31	52	76	29	79	194	39	18	100	90	50	152	34	1991	11
	maple	67	48	36	71	0	.	23	100	34	71	59	65	36	71	36	71	53	74	0	.	51	46	18	100	42	62	0	.	455	20
	ash	34	64	17	100	11	100	33	71	50	55	0	.	48	53	30	80	37	71	0	.	71	54	18	100	49	56	0	.	398	20
	oak	17	100	14	100	0	.	0	.	69	63	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	111	72	228	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1034	22	1034	22
	other broadleaves	93	42	24	77	34	71	17	100	73	80	33	71	89	55	50	62	0	.	41	61	57	53	183	44	275	43	481	33	1451	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1139	13	245	27	214	28	384	22	575	28	412	28	911	16	658	19	482	22	259	33	624	23	1288	15	1369	16	2107	15	10668	5
dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	518	25	89	45	540	31	111	41	384	25	709	33	1428	19	1334	23	1188	23	524	28	745	28	2193	17	2427	17	620	22	12811	7
	fir	1297	20	157	35	76	56	181	39	95	52	102	48	345	26	300	27	200	38	198	38	126	65	134	51	128	50	208	38	3548	10
	pine	109	45	112	62	17	100	0	.	48	54	0	.	45	100	0	.	169	70	43	71	0	.	1021	26	427	32	7	100	1997	17
	larch	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	79	1019	26	382	33	350	30	1862	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	43	61	149	49	0	.	199	39
	other conifers	19	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	79	0	.	106	57
	beech	496	21	204	36	135	59	116	42	173	41	21	100	75	44	261	33	184	48	37	71	131	39	0	.	48	73	252	44	2134	12
	maple	60	52	7	100	17	100	0	.	50	58	0	.	15	100	24	100	98	56	24	100	103	60	37	71	0	.	0	.	434	24
	ash	122	51	0	.	34	71	17	100	84	60	0	.	46	73	107	47	51	58	0	.	79	83	0	.	18	100	18	100	576	22
	oak	229	48	52	58	111	58	34	71	50	58	22	100	21	100	0	.	18	100	0	.	0	.	35	71	0	.	116	47	689	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2013	19	2013	19
	other broadleaves	439	25	87	53	266	62	115	61	341	38	298	32	284	34	210	52	285	48	54	58	101	53	296	30	635	31	1238	16	4650	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3290	12	725	21	1236	23	576	21	1226	17	1151	24	2259	14	2236	18	2199	17	880	21	1358	20	4777	13	4284	12	4823	10	31018	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20422	8	3949	20	7312	15	12944	12	18366	10	13988	13	22709	9	17729	11	12936	12	5962	19	6384	21	18949	10	30914	7	9710	11	202275	3
	fir	14748	8	1396	26	1937	25	7072	12	3817	14	3155	21	9834	10	4307	13	1846	22	1931	25	887	28	1358	27	892	26	1468	26	54648	4
	pine	1822	21	1137	28	702	39	640	30	1555	16	0	.	808	55	80	46	326	55	473	52	0	.	6639	19	5092	18	312	55	19587	9
	larch	82	67	153	46	240	50	149	61	534	29	0	.	23	72	147	71	293	32	7	100	349	57	8615	13	4558	12	5102	14	20253	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	1432	25	2483	20	16	100	3954	15
	other conifers	230	39	292	57	144	85	368	35	418	44	15	100	45	62	74	50	17	100	70	78	65	77	277	82	264	51	8	100	2288	18
	beech	21454	7	7228	13	4309	16	8744	10	10143	9	2507	28	9635	12	7788	14	3440	21	1749	25	2769	21	2026	30	2018	25	10888	14	94696	3
	maple	3883	11	1287	19	377	37	719	22	2857	15	1062	24	1890	18	2238	17	1801	23	480	32	1397	27	835	31	455	35	532	29	19814	5
	ash	2770	20	1071	28	1710	24	1480	26	3274	13	858	33	1385	26	2503	20	616	35	455	39	829	49	711	30	655	60	1333	25	19649	7
	oak	2501	27	786	29	2521	30	877	25	1305	18	168	57	228	57	116	59	197	60	58	57	0	.	655	34	84	74	2264	26	11760	11
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	176	61	28	100	13761	13	13982	12
	other broadleaves	3735	14	1260	22	1642	29	1834	27	3112	15	1305	22	1890	21	2410	24	2040	20	916	37	1160	28	4074	17	3890	22	14209	10	43479	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	71648	4	18559	10	20912	10	34827	7	45382	6	23058	11	48447	6	37391	8	23534	9	12101	14	13842	14	45746	7	51332	6	59604	5	506383	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. 1258986. Birmensdorf, Swiss Federal Research Institute WSL

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