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
grid: 1.4-km grid
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	14882	6	2787	16	5887	12	10210	11	15345	8	10355	10	16612	7	12640	8	9027	10	3974	15	4436	16	11645	8	23777	6	7571	9	149148	2
	fir	10952	6	1051	20	1685	19	4901	11	3490	11	2764	16	6812	8	3901	10	1009	19	1081	22	650	25	1171	22	636	20	1056	20	41161	3
	pine	1441	17	796	24	534	31	382	27	1513	14	0	.	710	39	95	35	151	43	415	34	256	57	4353	16	4251	14	347	46	15244	7
	larch	77	56	155	38	300	37	218	45	569	29	17	100	32	62	102	58	173	31	4	100	253	36	7137	9	3909	9	4195	10	17143	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	74	100	20	100	8	71	34	100	0	.	1065	23	1761	18	10	100	2972	14
	other conifers	291	44	185	57	208	70	282	30	411	34	9	100	46	72	31	58	10	100	41	79	0	.	62	85	222	38	4	100	1803	16
	beech	14333	5	6173	9	4414	11	6697	9	8529	7	1883	19	6686	10	5909	10	2778	21	1087	22	1842	21	1501	24	1124	21	9378	10	72334	3
	maple	3030	9	1018	15	803	28	782	19	2441	13	829	18	1115	13	1763	14	1507	17	355	27	724	26	533	21	362	33	632	23	15895	4
	ash	2168	15	1182	20	1506	17	1141	21	2801	11	688	26	951	21	1924	16	385	26	348	31	332	40	631	22	541	40	1558	18	16155	5
	oak	2011	22	803	24	1824	24	640	19	1273	18	102	42	70	47	92	41	388	44	63	44	0	.	532	24	148	61	1799	20	9745	8
	sweet chestnut	0	.	0	.	67	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	50	21	83	6675	10	6951	10
	other broadleaves	2728	12	1019	19	1184	21	974	22	2919	12	715	20	1372	20	1776	19	1618	16	607	29	831	24	2921	14	2167	17	10600	8	31434	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	51914	3	15169	7	18413	7	26226	6	39292	4	17362	8	34481	5	28253	6	17056	8	8009	12	9324	12	31739	5	38921	5	43825	3	379984	1
slight damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1386	12	108	27	699	16	702	13	1062	16	1451	16	1752	10	1292	15	1532	14	698	21	460	20	2530	10	2704	8	721	14	17100	4
	fir	466	15	41	50	39	42	384	19	152	24	110	37	501	16	209	23	32	44	93	40	54	42	61	38	42	50	72	44	2254	7
	pine	96	37	12	58	9	71	4	100	56	36	10	100	6	100	0	.	21	100	38	48	18	76	315	25	154	37	17	80	756	15
	larch	0	.	10	100	0	.	10	100	17	62	0	.	0	.	0	.	7	100	0	.	19	100	517	20	185	21	190	24	955	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	121	44	206	25	4	100	335	22
	other conifers	30	75	0	.	39	100	0	.	43	50	0	.	0	.	10	100	0	.	10	100	9	100	0	.	21	100	0	.	162	35
	beech	1073	12	298	21	307	28	327	20	369	19	175	66	756	24	345	19	170	34	216	27	219	31	168	39	109	33	515	20	5046	7
	maple	166	24	101	30	33	53	51	60	102	33	62	44	138	30	147	26	139	32	33	52	70	37	58	41	32	58	70	47	1201	10
	ash	128	28	19	83	50	51	28	38	150	28	13	75	96	55	158	38	43	46	9	100	14	100	33	52	10	100	159	28	910	13
	oak	85	38	62	54	100	62	8	71	41	48	20	74	94	74	12	73	0	.	0	.	0	.	24	61	9	71	52	44	507	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	78	0	.	780	14	799	14
	other broadleaves	229	29	118	44	69	42	75	45	140	36	64	51	228	34	105	40	196	33	134	49	49	45	570	24	371	29	799	13	3147	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3658	7	770	15	1344	14	1588	9	2132	10	1905	15	3575	9	2279	10	2141	11	1232	15	913	15	4416	8	3841	7	3378	6	33173	2
moderate damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1481	11	173	29	449	21	543	15	903	12	949	19	1653	10	1255	15	1053	14	501	21	688	20	1437	11	2062	10	627	18	13774	4
	fir	1138	11	40	42	102	39	405	17	223	22	158	28	1310	14	316	18	151	32	146	30	89	37	212	30	66	34	55	44	4409	6
	pine	104	28	38	41	18	64	12	58	72	33	0	.	57	45	13	58	25	85	70	42	20	71	278	21	304	24	30	52	1041	11
	larch	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	14	78	338	17	421	19	399	20	1185	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	142	30	152	24	0	.	305	19
	other conifers	50	82	10	100	0	.	24	61	23	71	0	.	9	100	20	71	0	.	21	100	0	.	21	100	51	66	0	.	228	29
	beech	2667	9	869	17	341	23	608	16	663	15	305	25	757	16	771	19	378	32	319	26	339	21	182	34	174	28	829	18	9203	5
	maple	429	15	164	33	63	54	39	50	157	25	122	33	191	27	317	29	106	45	73	54	137	29	117	44	34	53	53	42	2002	9
	ash	164	30	92	39	52	49	65	37	110	32	9	100	89	43	105	34	40	50	30	58	107	57	67	51	48	52	128	30	1104	12
	oak	53	42	16	50	37	38	58	45	49	39	0	.	4	100	10	100	10	100	8	70	0	.	44	46	0	.	89	36	377	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	896	13	906	13
	other broadleaves	367	20	171	34	75	50	160	33	257	21	67	38	151	39	165	34	118	33	123	36	125	41	548	19	338	26	1202	11	3868	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6463	5	1573	13	1137	13	1914	9	2456	8	1609	13	4231	7	2974	11	1884	12	1291	15	1518	13	3395	8	3651	7	4307	6	38401	2
severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1241	10	84	26	345	17	531	15	551	14	973	14	1398	11	1074	19	802	16	374	19	587	19	1180	11	1879	9	594	16	11614	4
	fir	925	11	47	39	109	29	404	15	226	19	267	28	856	14	302	21	146	31	97	38	49	38	177	26	131	28	105	30	3840	6
	pine	141	31	12	58	24	61	19	50	113	33	0	.	43	58	14	73	14	78	98	50	18	71	501	20	365	21	44	51	1406	11
	larch	14	77	10	100	0	.	4	100	4	100	0	.	8	71	9	100	15	77	0	.	32	58	548	16	293	19	306	18	1243	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	9	100	4	100	0	.	5	100	0	.	287	25	260	27	0	.	563	18
	other conifers	20	100	39	61	10	100	0	.	14	77	0	.	12	100	12	100	0	.	10	100	9	100	21	100	72	62	0	.	218	29
	beech	1572	10	416	17	230	25	339	19	448	15	61	37	697	20	396	17	319	25	140	30	240	32	108	37	237	28	353	19	5556	5
	maple	230	22	49	44	35	59	61	37	105	35	56	60	123	44	129	34	78	34	23	58	143	35	34	53	43	46	37	53	1145	11
	ash	195	34	41	55	103	48	27	59	154	26	5	100	86	44	99	44	44	50	40	50	122	39	76	43	10	100	151	38	1153	12
	oak	55	40	18	63	63	38	44	44	94	32	15	77	0	.	0	.	5	100	5	100	0	.	82	55	0	.	135	31	514	16
	sweet chestnut	0	.	0	.	14	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	71	0	.	2074	13	2108	13
	other broadleaves	308	21	50	42	79	53	134	35	174	31	99	46	168	37	109	33	170	33	78	44	58	47	298	28	366	28	922	14	3013	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4701	6	765	13	1012	13	1562	9	1882	8	1475	12	3400	8	2148	11	1592	12	869	14	1258	14	3331	8	3655	7	4722	7	32373	2
very severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	308	18	26	49	97	31	80	32	74	29	215	26	290	21	180	22	257	24	123	28	160	25	459	16	530	15	172	23	2971	6
	fir	233	18	14	77	4	100	74	40	73	33	76	35	202	24	57	38	45	52	45	75	52	48	47	40	19	66	41	41	984	10
	pine	49	39	0	.	0	.	33	76	9	72	10	100	18	80	5	100	0	.	14	77	10	100	186	33	145	31	19	100	497	18
	larch	0	.	0	.	11	100	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	170	24	66	32	150	28	409	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	36	94	35	0	.	196	25
	other conifers	0	.	10	100	0	.	4	100	10	100	0	.	10	100	10	100	0	.	22	71	19	71	62	71	196	59	0	.	344	37
	beech	525	17	62	33	63	38	77	32	188	28	31	45	240	22	191	24	45	53	28	56	172	33	10	100	91	39	210	26	1932	8
	maple	46	43	35	52	0	.	14	100	30	58	37	59	49	45	62	53	52	49	0	.	37	38	10	100	23	62	19	82	415	16
	ash	41	49	9	100	25	58	38	60	28	54	0	.	33	48	23	66	39	61	4	100	56	45	14	77	28	55	22	71	361	17
	oak	14	75	8	100	42	79	0	.	60	50	0	.	0	.	0	.	5	100	0	.	0	.	31	74	0	.	119	47	279	28
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	929	16	938	16
	other broadleaves	139	31	45	47	94	71	59	50	67	53	23	61	124	43	84	37	29	74	43	46	72	44	210	28	268	37	516	21	1774	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1356	10	208	22	340	25	379	20	538	18	392	19	966	12	626	15	472	18	278	23	579	18	1307	11	1459	13	2198	10	11099	4
dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	660	16	110	32	390	26	136	42	362	20	752	26	1410	14	1227	16	965	18	423	23	1100	25	2168	13	2205	12	832	19	12740	5
	fir	1126	15	159	31	94	39	223	33	152	31	136	30	356	20	282	21	148	32	160	30	94	52	265	33	90	45	238	31	3523	8
	pine	147	30	75	53	20	71	0	.	72	48	0	.	76	49	43	100	98	72	49	47	13	100	991	19	832	23	55	63	2470	12
	larch	30	100	0	.	21	100	0	.	14	76	10	100	0	.	0	.	10	100	0	.	40	80	957	19	447	22	629	17	2159	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	97	42	156	35	0	.	256	26
	other conifers	11	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	63	0	.	100	52
	beech	465	19	195	28	129	44	94	35	140	33	51	53	69	36	286	23	117	44	98	49	125	35	58	62	78	43	302	27	2209	9
	maple	113	32	14	77	33	53	0	.	54	42	0	.	9	100	23	73	76	46	14	100	69	52	29	58	0	.	11	100	445	17
	ash	135	35	0	.	70	38	10	100	60	53	45	51	26	72	83	43	30	58	10	100	58	70	15	100	10	100	71	56	623	16
	oak	178	37	58	47	121	40	19	71	86	36	13	100	12	100	0	.	20	71	0	.	10	100	20	71	30	74	179	43	745	17
	sweet chestnut	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	1816	15	1831	15
	other broadleaves	396	18	102	43	261	43	95	48	402	35	258	29	351	29	191	34	352	29	50	53	92	44	485	25	557	24	1254	14	4846	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3262	8	724	16	1149	17	577	23	1341	14	1265	18	2310	10	2134	12	1820	13	804	17	1600	19	5089	9	4485	9	5387	7	31948	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19958	6	3287	15	7867	11	12201	10	18299	7	14694	10	23116	7	17668	8	13636	9	6093	14	7431	14	19418	7	33159	5	10518	9	207347	2
	fir	14840	6	1352	19	2033	18	6392	10	4316	10	3510	14	10037	7	5067	10	1531	18	1621	20	990	22	1932	20	983	18	1566	19	56170	3
	pine	1979	16	934	22	604	29	449	26	1834	13	21	100	911	35	169	39	308	43	685	33	334	54	6623	14	6051	13	512	41	21415	6
	larch	130	65	175	35	332	37	232	45	603	28	27	100	41	59	124	52	210	27	4	100	358	37	9667	8	5322	9	5869	9	23094	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	98	100	24	100	12	58	38	100	0	.	1813	18	2629	16	13	77	4628	12
	other conifers	401	41	253	48	257	62	310	29	501	30	9	100	77	49	84	39	10	100	104	53	38	78	165	82	642	35	4	100	2854	15
	beech	20636	5	8014	9	5484	11	8142	8	10337	7	2505	19	9205	9	7899	10	3808	18	1888	20	2937	18	2027	22	1813	19	11587	10	96281	2
	maple	4014	8	1381	14	968	27	947	18	2888	13	1106	18	1624	13	2441	13	1958	16	499	24	1180	21	781	21	493	27	822	21	21103	4
	ash	2832	14	1343	19	1807	17	1309	19	3303	10	760	25	1281	19	2392	16	580	25	441	29	688	38	836	21	646	37	2090	16	20306	5
	oak	2396	20	965	25	2187	22	768	19	1603	17	150	45	179	43	115	40	428	42	76	43	10	100	733	23	187	61	2373	18	12168	8
	sweet chestnut	0	.	0	.	94	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	249	50	21	83	13169	9	13533	9
	other broadleaves	4167	10	1504	18	1763	20	1496	19	3959	11	1227	17	2395	17	2432	18	2483	15	1037	28	1227	23	5031	13	4067	17	15293	7	48081	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	71354	3	19209	7	23396	7	32245	6	47642	4	24008	8	48963	5	38414	6	24965	7	12484	11	15193	10	49276	5	56013	4	63817	3	526978	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. 1259034. Birmensdorf, Swiss Federal Research Institute WSL

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