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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1290	10	3458	9	3674	6	794	10	1428	9	496	19	11140	4
	fir	992	10	953	11	1130	8	71	53	46	22	67	30	3259	5
	pine	61	39	126	34	76	33	438	20	303	18	16	58	1020	11
	larch	23	89	72	44	18	49	292	15	132	22	263	28	799	12
	Arolla pine	0	.	0	.	4	83	55	36	72	26	2	75	133	21
	other conifers	55	35	82	37	27	27	2	58	32	41	10	100	208	19
	beech	1609	8	1841	7	1590	10	77	40	134	28	989	13	6241	4
	maple	394	12	361	17	667	11	30	48	53	36	47	31	1551	7
	ash	295	16	549	16	454	13	54	29	63	45	214	20	1628	8
	oak	119	31	311	22	40	31	50	26	5	75	206	34	732	14
	sweet chestnut	1	100	5	74	16	81	1	100	0	.	1004	13	1027	12
	other broadleaves	407	13	602	12	930	10	296	17	255	16	1190	9	3679	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5245	4	8360	5	8626	4	2160	7	2522	7	4504	6	31417	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2503	8	5238	7	9204	5	1965	12	3885	8	1000	13	23795	3
	fir	1926	9	2140	10	2239	9	147	37	117	24	122	28	6691	5
	pine	283	24	221	19	256	25	538	18	551	16	86	54	1934	9
	larch	36	57	227	27	29	49	869	14	548	16	661	15	2370	8
	Arolla pine	0	.	0	.	27	63	179	25	257	23	4	100	466	16
	other conifers	35	50	119	38	1	2781	0	.	13	56	0	.	167	31
	beech	4682	7	3873	7	3224	8	82	38	311	26	998	14	13170	4
	maple	884	11	997	11	1288	10	-3	564	80	43	98	32	3344	6
	ash	662	17	1294	11	890	15	37	46	69	39	174	25	3127	7
	oak	342	22	746	16	120	32	30	44	22	90	252	21	1512	10
	sweet chestnut	0	.	8	73	13	76	20	72	0	.	1105	12	1145	12
	other broadleaves	360	15	878	13	794	12	160	23	132	25	1133	10	3457	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11712	4	15741	4	18086	4	4024	7	5984	6	5631	5	61179	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4664	8	9738	6	14923	5	2893	10	5564	7	1821	12	39604	3
	fir	3603	8	3514	8	3993	7	177	34	144	28	191	39	11623	4
	pine	881	16	1233	14	328	24	535	17	725	17	161	42	3862	7
	larch	67	47	239	29	115	45	1266	12	967	13	843	14	3498	7
	Arolla pine	0	.	0	.	22	70	189	23	351	21	0	.	562	15
	other conifers	0	.	125	34	0	.	0	.	0	.	7	100	132	33
	beech	6490	6	5899	6	4149	8	101	52	378	26	849	13	17866	3
	maple	790	13	764	14	904	11	23	77	67	40	24	68	2572	7
	ash	733	18	1784	12	682	16	26	70	38	42	84	38	3347	8
	oak	382	19	1055	14	95	54	8	100	35	90	177	27	1751	10
	sweet chestnut	0	.	14	100	7	100	16	72	0	.	576	16	613	16
	other broadleaves	296	23	820	16	445	19	71	37	90	34	645	16	2368	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17905	4	25184	3	25663	3	5305	7	8360	6	5379	6	87796	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5886	6	12617	5	17966	4	2281	10	6689	7	1978	12	47417	2
	fir	3663	7	4807	7	5243	7	134	40	368	21	345	26	14560	4
	pine	704	16	1496	11	361	33	335	25	724	17	63	50	3682	8
	larch	42	52	376	23	202	32	1813	11	1395	11	926	13	4753	6
	Arolla pine	0	.	0	.	1	100	171	27	272	19	6	100	449	15
	other conifers	0	.	136	37	0	.	0	.	0	.	0	.	136	37
	beech	5240	7	6100	6	4215	8	41	59	351	27	610	15	16557	4
	maple	364	17	365	19	528	15	0	.	27	60	13	73	1297	10
	ash	325	20	1507	12	389	17	17	58	28	50	-4	697	2262	9
	oak	269	27	1183	13	25	51	9	73	63	65	153	40	1701	11
	sweet chestnut	0	.	11	100	-3	100	0	.	0	.	460	20	468	20
	other broadleaves	110	31	435	17	283	29	15	90	3	663	175	19	1021	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16603	4	29031	3	29210	3	4816	7	9917	6	4725	6	94303	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4609	7	9522	6	14420	5	1600	14	5194	8	1762	11	37107	3
	fir	2942	10	3946	9	4278	8	191	39	340	25	203	32	11900	5
	pine	404	21	936	15	98	49	229	36	430	24	47	51	2145	10
	larch	25	71	280	26	283	32	1444	12	1246	13	964	13	4241	7
	Arolla pine	0	.	0	.	0	0	44	42	202	25	0	.	246	22
	other conifers	33	100	105	60	0	.	0	.	0	.	0	.	138	52
	beech	2604	10	4339	8	2918	12	25	59	217	32	334	25	10436	5
	maple	76	36	268	25	165	31	0	.	17	71	0	.	526	17
	ash	110	35	729	17	200	32	0	.	0	.	21	100	1060	14
	oak	204	35	1113	15	91	36	7	100	8	100	41	60	1464	13
	sweet chestnut	0	.	0	.	34	84	0	.	0	.	398	20	432	20
	other broadleaves	64	56	140	34	219	33	27	75	0	.	56	60	507	20
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11072	5	21377	4	22705	4	3566	9	7654	6	3828	7	70201	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1899	12	4535	9	7398	7	676	22	2502	11	1078	18	18088	4
	fir	1256	14	1303	15	2352	12	116	36	208	33	198	40	5433	7
	pine	95	46	298	26	50	62	9	100	103	46	10	100	564	19
	larch	0	.	126	42	207	40	797	17	435	21	518	19	2084	10
	Arolla pine	0	.	0	.	10	100	57	42	98	35	0	.	165	26
	other conifers	20	100	115	48	0	.	0	.	0	.	0	.	135	44
	beech	783	21	1993	14	641	21	0	.	49	74	151	32	3617	10
	maple	50	59	34	100	34	58	0	.	0	.	0	.	118	42
	ash	42	58	345	29	17	100	0	.	26	100	0	.	430	25
	oak	119	49	391	26	0	.	0	.	7	100	3	83	520	23
	sweet chestnut	0	.	0	.	12	100	0	.	0	.	167	27	179	26
	other broadleaves	0	.	151	39	28	71	0	.	0	.	26	72	205	32
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4263	8	9292	6	10749	6	1655	13	3428	9	2153	11	31540	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	700	21	1113	18	2909	11	284	28	1147	17	633	23	6787	7
	fir	574	24	852	20	682	20	0	.	154	46	84	58	2345	11
	pine	0	.	23	100	0	.	0	.	35	72	0	.	58	59
	larch	22	100	78	60	21	100	503	23	270	28	279	27	1173	14
	Arolla pine	0	.	0	.	0	.	48	45	65	41	16	100	129	29
	other conifers	0	.	81	59	0	.	0	.	0	.	0	.	81	59
	beech	196	34	1016	20	135	40	0	.	0	.	106	40	1454	15
	maple	0	.	36	100	21	71	0	.	18	100	6	100	82	53
	ash	0	.	60	64	0	.	0	.	0	.	0	.	60	64
	oak	0	.	257	33	57	61	0	.	0	.	0	.	313	29
	sweet chestnut	0	.	0	.	0	.	15	100	0	.	215	28	229	27
	other broadleaves	0	.	57	75	0	.	0	.	0	.	26	72	82	56
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1492	15	3573	11	3825	9	851	17	1690	14	1365	14	12795	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	163	45	470	28	883	19	143	41	260	31	188	37	2107	12
	fir	119	50	389	32	580	26	0	.	28	100	67	72	1182	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	146	38	82	51	168	47	397	27
	Arolla pine	0	.	0	.	0	.	13	100	17	100	0	.	30	71
	other conifers	0	.	35	100	0	.	0	.	0	.	0	.	35	100
	beech	59	71	371	35	84	50	21	100	12	100	27	71	576	25
	maple	0	.	0	.	39	72	0	.	0	.	0	.	39	72
	ash	0	.	64	71	0	.	0	.	0	.	0	.	64	71
	oak	0	.	253	37	0	.	0	.	0	.	0	.	253	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	205	34	205	34
	other broadleaves	0	.	82	72	0	.	0	.	0	.	20	100	102	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	341	30	1664	16	1586	14	323	27	399	24	677	20	4990	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	33	100	131	58	320	30	26	100	66	71	134	54	710	22
	fir	31	100	36	100	199	45	0	.	44	100	0	.	309	36
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	29	100	0	.	61	58	0	.	81	58	171	39
	Arolla pine	0	.	0	.	0	.	19	100	12	100	0	.	31	72
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	178	50	0	.	0	.	0	.	0	.	178	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	34	100	0	.	0	.	0	.	0	.	34	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	176	44	176	44
	other broadleaves	0	.	34	100	0	.	0	.	0	.	0	.	34	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	64	71	441	30	519	25	107	45	122	53	390	30	1643	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	47	100	166	48	0	.	142	51	130	65	485	30
	fir	0	.	42	100	50	100	0	.	0	.	101	71	193	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	33	100	41	72	133	50	207	39
	Arolla pine	0	.	0	.	0	.	37	71	18	100	0	.	55	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	0	0	.	0	.	232	100	232	100
	maple	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	35	100	0	.	0	.	0	.	0	.	35	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	657	38	657	38
	other broadleaves	40	100	0	.	0	.	0	.	0	.	0	.	40	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	40	100	125	58	233	41	70	60	201	40	1253	29	1922	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21746	5	46868	4	71863	3	10663	8	26877	5	9221	8	187239	2
	fir	15106	6	17982	6	20746	5	836	26	1448	17	1378	21	57496	3
	pine	2427	12	4332	10	1169	21	2084	15	2870	13	382	36	13264	6
	larch	214	34	1426	17	873	27	7225	8	5116	9	4838	9	19692	5
	Arolla pine	0	.	0	.	64	58	812	17	1364	15	27	80	2267	11
	other conifers	142	44	800	24	27	60	2	58	45	40	16	72	1033	19
	beech	21663	4	25611	4	16956	6	347	30	1452	20	4297	10	70327	2
	maple	2558	8	2825	9	3665	7	50	38	261	31	187	25	9546	4
	ash	2167	12	6331	8	2632	10	135	31	224	33	489	19	11979	5
	oak	1434	15	5377	9	428	22	103	27	140	56	833	18	8315	7
	sweet chestnut	1	100	38	84	79	79	51	61	0	.	4964	10	5133	10
	other broadleaves	1277	11	3197	10	2700	9	569	17	479	16	3272	8	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68736	2	114788	2	121203	2	22877	5	40277	4	29904	3	397785	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. 1217923. Birmensdorf, Swiss Federal Research Institute WSL

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