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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NFI4
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 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
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	13563	9	2278	22	3857	21	7496	13	10882	11	6635	15	14950	10	10431	11	8628	13	3967	19	4111	19	13109	11	23811	8	8021	11	131738	3
	fir	12418	8	1180	31	1584	24	5297	13	3417	18	2575	23	8007	10	3409	15	1518	23	1463	28	462	28	1157	25	634	30	1215	27	44336	4
	pine	1138	24	611	31	307	38	581	50	797	18	0	.	799	60	37	55	107	56	289	55	0	.	4388	22	3174	21	202	62	12427	11
	larch	31	64	70	44	543	66	119	44	399	29	88	100	94	93	197	78	432	40	0	.	122	63	6404	12	3564	13	4563	14	16625	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1200	26	1867	22	49	100	3125	16
	other conifers	263	38	214	50	133	71	333	30	519	43	0	.	57	52	56	58	17	100	52	100	15	100	35	71	194	52	16	100	1904	17
	beech	14760	7	5005	13	3422	15	5905	10	7727	8	1782	26	6710	12	5210	13	2039	25	1201	27	1954	26	1668	31	1219	26	9334	15	67936	4
	maple	3881	13	970	19	524	30	898	25	3071	17	893	24	1533	21	1837	20	1759	22	401	37	716	28	666	32	486	37	560	30	18195	6
	ash	1665	17	1351	26	1585	21	1279	20	2925	14	639	35	1107	28	1803	20	536	31	568	37	284	41	822	43	542	52	1660	27	16765	7
	oak	1886	30	507	31	1164	32	416	26	662	19	89	57	68	65	29	76	158	70	55	68	0	.	847	35	91	72	1856	27	7830	12
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	62	66	6	100	6375	15	6457	15
	other broadleaves	2363	16	724	22	950	26	1609	21	2537	16	621	26	963	21	1024	23	1620	24	536	29	838	33	2880	18	2898	25	10160	13	29723	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	51967	4	12909	10	14068	11	23933	8	32943	6	13322	12	34287	7	24034	8	16831	10	8533	15	8501	15	33239	7	38484	6	44010	5	357061	2
slight damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1018	16	437	35	404	30	874	22	1113	15	1210	21	1294	13	937	22	1071	16	718	24	700	22	2178	12	3923	10	1160	17	17037	4
	fir	381	23	58	67	54	57	124	35	77	47	89	42	247	26	149	34	83	54	141	35	94	47	58	57	45	62	47	53	1646	10
	pine	90	50	7	100	0	.	24	77	14	71	0	.	0	.	0	.	35	71	7	100	0	.	209	36	139	39	0	.	526	21
	larch	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	45	73	0	.	6	100	244	31	256	27	301	32	859	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	43	327	28	0	.	411	24
	other conifers	17	100	7	100	0	.	17	100	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	58	0	.	130	36
	beech	665	21	296	26	115	37	288	27	154	35	188	56	402	38	468	27	179	33	122	57	152	39	171	67	118	44	444	25	3761	9
	maple	208	29	52	58	25	76	72	62	101	47	65	53	153	34	145	33	125	39	0	.	156	45	55	58	6	100	122	50	1284	13
	ash	75	63	43	61	101	39	31	64	89	45	0	.	47	75	160	47	37	70	61	76	37	71	79	50	56	58	161	54	977	17
	oak	50	55	16	71	102	50	19	100	38	55	0	.	31	77	21	100	0	.	0	.	0	.	40	71	0	.	0	.	316	24
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	379	27	395	27
	other broadleaves	169	36	43	61	41	61	101	42	139	56	17	100	142	43	119	41	136	67	54	74	87	48	251	31	375	37	787	19	2461	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2674	10	958	20	857	19	1550	15	1767	12	1570	18	2315	11	1999	15	1711	14	1103	20	1231	17	3370	10	5299	8	3400	9	29802	3
moderate damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	669	17	66	48	148	37	354	26	349	24	479	21	798	16	736	19	789	19	364	34	351	26	735	18	1321	13	458	29	7618	5
	fir	692	18	24	77	37	53	260	31	87	36	132	62	544	20	120	35	149	35	177	37	25	79	74	57	40	55	57	76	2419	10
	pine	7	100	14	71	27	50	7	100	20	58	0	.	13	71	9	100	0	.	7	100	0	.	197	40	290	32	16	100	608	21
	larch	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	205	28	106	36	244	29	562	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	71	94	51	0	.	129	41
	other conifers	69	61	17	100	0	.	39	100	34	71	0	.	0	.	18	100	0	.	0	.	15	100	0	.	0	.	0	.	192	36
	beech	1710	18	538	21	275	30	336	26	513	19	103	44	871	21	739	24	450	29	162	42	297	31	182	42	194	33	839	21	7209	7
	maple	327	29	126	40	34	71	64	63	230	39	85	48	226	32	241	36	156	31	65	71	41	61	136	49	32	71	130	42	1894	12
	ash	90	48	43	61	81	43	40	61	111	39	69	69	67	48	18	100	18	100	85	61	75	47	40	61	36	71	94	52	867	15
	oak	73	44	0	.	58	66	30	64	39	57	0	.	0	.	8	100	18	100	18	100	0	.	0	.	0	.	42	61	285	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	1035	18	1050	18
	other broadleaves	181	38	85	55	68	61	51	58	214	34	15	100	74	50	251	43	102	41	43	62	40	61	335	27	256	39	1182	19	2898	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3817	10	913	18	729	19	1180	14	1603	12	883	18	2593	12	2140	14	1682	14	924	23	843	19	1940	12	2385	10	4098	9	25732	3
severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	909	15	132	48	179	29	243	23	450	20	491	23	728	18	666	21	574	18	291	25	225	32	903	15	1200	14	525	19	7517	5
	fir	834	18	0	.	43	54	264	28	98	31	113	39	457	21	253	31	101	40	68	69	47	55	105	38	77	39	113	35	2570	9
	pine	79	46	16	71	13	100	7	100	37	55	0	.	35	100	0	.	0	.	37	71	0	.	222	31	288	34	16	100	750	18
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	79	284	23	165	32	211	34	691	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	42	96	45	0	.	270	31
	other conifers	52	74	35	70	0	.	0	.	36	71	0	.	0	.	39	71	0	.	0	.	15	100	0	.	36	71	0	.	214	31
	beech	1313	15	338	25	140	39	387	20	422	25	77	60	702	25	371	41	181	36	254	42	254	43	92	49	127	42	865	20	5523	8
	maple	203	29	24	77	17	100	34	82	159	32	93	51	200	35	161	45	175	34	15	100	185	31	117	54	151	43	61	52	1598	12
	ash	165	30	21	58	41	85	43	54	77	42	40	71	117	38	170	39	112	54	56	68	32	65	55	53	136	45	56	58	1121	14
	oak	107	50	24	77	54	49	67	44	80	47	0	.	0	.	0	.	18	100	7	100	0	.	70	50	0	.	74	47	502	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	6	100	2757	19	2782	19
	other broadleaves	272	31	110	41	91	61	135	43	189	31	30	71	96	42	259	41	126	37	77	51	58	53	511	27	406	31	1008	16	3369	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3940	8	700	19	579	21	1180	13	1549	12	845	19	2335	12	1919	17	1287	14	807	21	840	21	2550	10	2689	11	5687	10	26906	4
very severe damage	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	174	37	0	.	20	58	72	49	46	54	84	44	285	28	94	50	231	25	60	46	98	40	205	26	330	29	83	36	1782	10
	fir	151	35	0	.	0	.	37	55	34	71	34	60	149	36	96	51	6	100	31	61	29	63	22	74	82	51	16	71	686	16
	pine	24	77	14	70	7	100	7	100	7	100	0	.	0	.	0	.	0	.	18	100	0	.	172	39	247	40	0	.	496	25
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	245	30	54	44	156	41	463	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	44	21	58	0	.	95	37
	other conifers	0	.	17	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	35	100	18	100	121	62	302	62	0	.	512	40
	beech	428	18	118	34	81	38	180	32	115	36	36	71	301	27	163	34	34	65	38	60	121	57	81	67	68	64	425	27	2188	10
	maple	95	45	24	77	0	.	7	100	184	32	150	73	59	58	8	100	105	47	15	100	33	55	59	58	25	79	35	71	801	19
	ash	21	57	24	77	57	52	24	77	62	50	39	71	81	51	78	44	92	55	18	100	92	62	24	79	37	71	16	71	665	18
	oak	60	52	7	100	0	.	32	81	63	69	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	111	52	292	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	1426	20	1434	20
	other broadleaves	73	50	7	100	0	.	17	100	78	57	0	.	134	41	98	45	140	35	17	100	80	45	348	51	242	42	612	27	1845	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1027	14	212	26	166	27	375	22	588	18	342	36	1009	15	562	20	608	22	233	29	472	25	1376	19	1408	21	2880	13	11258	6
dead	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1603	14	321	27	503	33	794	22	716	18	2280	19	3171	19	1978	22	2641	19	749	27	876	26	3109	16	3472	13	911	23	23124	5
	fir	1322	16	66	53	107	45	353	25	245	33	205	35	864	18	416	29	272	37	324	40	125	45	154	36	95	46	119	41	4667	8
	pine	317	32	116	51	138	54	47	76	79	41	0	.	58	90	0	.	179	61	24	100	0	.	942	21	634	30	87	53	2620	13
	larch	34	100	0	.	41	71	0	.	0	.	0	.	17	100	0	.	91	60	0	.	61	77	1342	20	480	23	516	24	2583	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	59	160	45	0	.	233	36
	other conifers	54	58	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	71	212	53	0	.	318	38
	beech	736	20	353	35	107	41	244	27	399	22	30	72	616	29	493	22	240	31	77	66	198	34	122	51	95	51	439	30	4150	9
	maple	124	38	49	63	25	76	70	61	174	41	33	71	53	58	109	65	80	52	15	100	81	51	117	52	18	100	37	71	985	16
	ash	125	38	52	58	33	71	47	54	179	40	18	100	162	39	113	46	74	50	22	100	22	100	110	58	94	60	231	46	1281	15
	oak	217	38	69	61	172	38	23	77	7	100	18	100	0	.	36	100	37	71	0	.	0	.	50	58	0	.	337	44	967	20
	sweet chestnut	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	3531	15	3588	15
	other broadleaves	524	24	111	38	247	47	17	100	315	27	360	37	532	34	384	37	558	29	187	51	279	40	837	22	919	24	2297	14	7566	8
	indeterminable	168	41	24	77	17	100	17	100	51	58	115	43	163	41	75	46	218	32	39	62	22	76	154	32	181	35	126	59	1370	13
	total	5224	8	1161	19	1427	18	1612	15	2165	12	3059	16	5636	13	3604	15	4390	14	1437	20	1665	18	7083	11	6359	10	8631	8	53452	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17936	8	3233	21	5110	19	9834	12	13557	10	11180	13	21226	9	14842	11	13935	12	6150	18	6362	18	20240	10	34056	7	11156	11	188816	3
	fir	15797	8	1328	29	1825	23	6334	12	3958	16	3148	22	10267	10	4443	14	2128	21	2205	25	782	27	1570	25	973	26	1567	25	56325	4
	pine	1654	23	777	27	491	36	672	44	954	17	0	.	905	57	46	48	321	57	383	51	0	.	6130	19	4773	19	321	57	17427	10
	larch	72	73	70	44	584	61	119	44	413	29	88	100	111	94	204	76	567	37	0	.	213	56	8725	11	4625	12	5992	14	21782	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1639	24	2565	20	49	100	4263	15
	other conifers	455	39	290	44	150	74	390	29	624	37	0	.	57	52	131	37	17	100	87	100	65	77	191	54	797	41	16	100	3270	15
	beech	19612	7	6647	12	4141	15	7339	10	9329	8	2216	27	9602	11	7444	13	3125	21	1855	26	2974	22	2316	28	1821	24	12345	13	90766	3
	maple	4838	11	1246	18	625	30	1145	23	3919	15	1320	22	2224	20	2501	18	2399	20	513	34	1212	24	1151	27	717	30	946	27	24756	5
	ash	2140	17	1534	24	1898	20	1465	19	3443	13	804	31	1580	24	2342	19	868	31	811	31	541	33	1129	35	902	46	2218	25	21677	6
	oak	2393	28	624	31	1550	30	587	23	888	17	107	50	99	52	94	51	231	65	81	59	0	.	1025	33	91	72	2422	26	10191	11
	sweet chestnut	0	.	0	.	36	71	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	126	75	28	100	15504	12	15707	12
	other broadleaves	3581	14	1080	19	1398	24	1928	21	3473	15	1044	23	1941	19	2135	25	2681	20	914	32	1382	31	5162	16	5096	22	16046	10	47861	5
	indeterminable	168	41	24	77	17	100	17	100	51	58	115	43	163	41	75	46	218	32	39	62	22	76	154	32	181	35	126	59	1370	13
	total	68648	4	16854	10	17826	11	29830	7	40615	6	20022	11	48176	6	34257	8	26508	9	13036	14	13552	14	49558	7	56624	6	68707	4	504211	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. 1259157. Birmensdorf, Swiss Federal Research Institute WSL

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