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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		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	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1205	12	1734	10	2033	7	4149	5	626	15	9748	4
	fir	886	9	884	13	1034	9	403	16	96	33	3303	6
	pine	24	60	29	76	24	97	639	13	7	66	723	13
	larch	12	77	36	49	28	51	683	11	260	16	1018	9
	Arolla pine	0	.	0	.	2	71	225	19	2	65	229	19
	other conifers	27	66	38	60	17	68	74	22	2	73	157	22
	beech	1320	8	1253	9	911	10	684	12	1024	11	5192	4
	maple	462	12	566	13	458	13	477	11	157	19	2120	6
	ash	290	16	512	16	228	18	385	17	242	19	1657	8
	oak	94	27	141	24	6	65	103	22	125	25	468	12
	sweet chestnut	0	.	5	62	0	.	15	76	667	16	687	15
	other broadleaves	383	13	617	11	377	14	1436	8	1314	9	4127	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4703	4	5815	5	5119	4	9272	3	4522	5	29431	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2417	9	3906	8	5140	6	9171	5	1163	12	21798	3
	fir	1606	9	1265	12	1922	9	835	14	159	28	5787	5
	pine	243	28	150	28	80	44	1143	12	47	47	1662	10
	larch	36	59	122	30	15	82	1445	9	753	14	2370	7
	Arolla pine	0	.	0	.	17	72	546	14	2	100	566	13
	other conifers	73	42	129	28	19	46	51	36	0	.	271	19
	beech	3382	7	2632	8	2379	8	1485	11	1547	12	11425	4
	maple	786	11	990	13	1070	12	857	13	127	28	3830	6
	ash	526	15	918	12	791	15	493	15	405	25	3132	7
	oak	489	21	371	17	29	43	118	26	242	23	1250	11
	sweet chestnut	0	.	7	100	0	.	18	56	1253	12	1278	12
	other broadleaves	454	16	756	12	359	17	1067	11	1498	10	4133	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	10012	4	11244	4	11821	4	17228	3	7197	5	57502	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3565	8	5054	7	8493	5	13862	4	2005	12	32979	3
	fir	2763	9	2068	10	3128	7	971	13	250	31	9180	5
	pine	675	17	586	16	174	31	1439	12	166	59	3041	8
	larch	59	36	284	27	24	50	2500	8	1130	12	3997	7
	Arolla pine	0	.	0	.	26	89	641	15	5	100	671	14
	other conifers	106	51	102	44	8	100	-9	144	0	.	208	35
	beech	6005	6	3904	7	3638	8	1527	10	1211	12	16286	4
	maple	966	12	751	15	818	12	651	14	94	36	3279	7
	ash	724	14	1535	11	821	16	382	21	278	24	3741	7
	oak	484	20	560	15	34	79	113	35	290	20	1481	10
	sweet chestnut	0	.	16	82	0	.	23	64	949	14	988	14
	other broadleaves	505	18	681	17	191	23	568	16	1116	12	3060	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	15851	3	15542	4	17355	3	22668	3	7494	5	78911	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5285	7	5357	7	11215	5	15541	4	2301	12	39700	2
	fir	3787	8	2778	9	4613	7	1588	12	292	23	13058	4
	pine	767	15	823	15	233	50	1104	12	111	47	3038	8
	larch	63	64	220	27	62	50	3679	8	1163	13	5187	7
	Arolla pine	0	.	0	.	14	100	509	14	0	.	523	14
	other conifers	105	53	140	42	0	.	0	.	11	100	256	32
	beech	6469	5	5047	6	3953	8	1599	13	954	12	18022	3
	maple	693	13	421	17	456	15	402	16	31	79	2004	8
	ash	701	16	1727	11	674	16	320	24	146	29	3568	7
	oak	463	20	944	15	48	52	148	31	191	31	1794	10
	sweet chestnut	0	.	9	100	0	.	16	51	595	15	620	14
	other broadleaves	281	23	517	20	127	29	190	29	504	16	1619	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	18614	3	17984	3	21396	3	25094	3	6300	6	89388	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4643	7	5543	8	11444	6	13664	5	1932	12	37227	3
	fir	3375	9	3424	9	5185	7	1708	13	442	27	14134	4
	pine	439	27	845	16	116	49	862	16	60	61	2321	10
	larch	55	49	388	24	145	43	4275	9	1172	11	6034	7
	Arolla pine	0	.	0	.	0	.	303	19	7	100	310	19
	other conifers	40	100	157	49	0	.	0	.	16	100	214	41
	beech	4426	7	4904	7	2923	10	1416	16	713	16	14382	4
	maple	263	22	298	26	231	27	179	33	21	71	992	13
	ash	416	20	1301	15	283	27	128	35	58	61	2186	11
	oak	288	27	998	16	46	58	103	38	66	41	1501	13
	sweet chestnut	0	.	39	100	0	.	5	100	526	19	570	19
	other broadleaves	205	48	288	31	58	46	389	37	109	34	1048	19
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	0
	total	14150	4	18185	4	20430	4	23032	3	5122	6	80919	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2706	10	2769	12	6528	7	7457	6	1172	16	20632	4
	fir	2338	12	2752	11	3551	11	1095	17	129	38	9864	6
	pine	152	38	331	28	98	48	265	28	10	100	856	16
	larch	17	100	204	38	84	76	2293	10	700	17	3298	9
	Arolla pine	0	.	0	.	11	100	225	24	0	.	236	23
	other conifers	0	.	216	37	0	.	0	.	0	.	216	37
	beech	1486	14	2635	10	1515	15	619	23	357	24	6612	7
	maple	20	100	115	42	53	78	56	54	0	.	243	30
	ash	180	33	502	27	143	38	66	58	44	71	936	18
	oak	254	41	533	21	38	71	17	100	28	65	870	18
	sweet chestnut	0	.	0	.	0	.	27	76	209	24	236	23
	other broadleaves	112	42	140	45	40	100	67	51	78	56	436	24
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	0
	total	7264	6	10198	5	12059	5	12187	5	2727	9	44435	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1151	17	1391	15	3534	10	5175	9	917	20	12168	6
	fir	1076	18	1146	19	2021	13	730	20	244	38	5217	8
	pine	16	100	24	100	0	.	55	59	0	.	95	46
	larch	24	100	0	.	98	52	1336	14	409	24	1868	12
	Arolla pine	0	.	0	.	0	.	133	31	0	.	133	31
	other conifers	25	100	201	51	0	.	0	.	0	.	226	47
	beech	712	21	1280	17	588	25	203	37	172	36	2955	11
	maple	34	100	0	.	8	100	57	58	0	.	99	49
	ash	96	52	314	34	92	58	43	71	0	.	544	24
	oak	126	46	320	39	16	100	34	61	16	100	512	27
	sweet chestnut	0	.	0	.	0	.	10	100	250	25	260	24
	other broadleaves	25	100	107	52	0	.	43	71	52	82	228	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3286	10	4784	9	6356	8	7818	7	2059	11	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	356	36	634	24	1061	22	1922	13	531	23	4505	9
	fir	349	32	496	27	764	23	298	34	70	71	1976	14
	pine	0	.	27	100	0	.	29	100	0	.	56	71
	larch	0	.	30	100	0	.	544	23	272	35	847	19
	Arolla pine	0	.	0	.	0	.	120	34	0	.	120	34
	other conifers	34	100	39	100	41	100	0	.	0	.	114	58
	beech	131	45	510	29	159	51	128	53	69	51	997	20
	maple	0	.	43	100	12	100	14	100	0	.	69	68
	ash	0	.	92	65	38	100	0	.	0	.	130	54
	oak	84	61	203	38	90	60	0	.	0	.	378	28
	sweet chestnut	0	.	0	.	0	.	17	100	175	34	192	33
	other broadleaves	84	77	30	100	0	.	0	.	10	100	124	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1038	19	2105	14	2167	15	3073	10	1127	16	9509	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	76	71	313	35	603	29	174	43	1202	19
	fir	245	48	64	71	158	58	152	50	79	71	698	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	351	30	61	100	412	29
	Arolla pine	0	.	0	.	0	.	34	71	0	.	34	71
	other conifers	0	.	44	100	0	.	0	.	0	.	44	100
	beech	37	100	83	72	70	58	0	.	54	71	244	37
	maple	0	.	0	.	0	.	0	.	11	100	11	100
	ash	0	.	34	100	0	.	0	.	0	.	34	100
	oak	71	73	95	100	0	.	0	.	0	.	165	65
	sweet chestnut	0	.	0	.	0	.	0	.	67	55	67	55
	other broadleaves	0	.	120	74	0	.	46	100	0	.	167	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	388	36	517	33	541	27	1186	19	446	28	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39	100	79	71	140	51	808	33	262	42	1329	23
	fir	0	.	123	58	135	71	124	73	68	71	450	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	104	51	157	64	260	43
	Arolla pine	0	.	0	.	0	.	40	71	0	.	40	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	214	100	214	100
	maple	0	.	0	.	0	.	74	77	0	.	74	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	39	100	66	71	0	.	0	.	0	.	105	58
	sweet chestnut	0	.	0	.	0	.	0	.	660	39	660	39
	other broadleaves	187	75	242	100	0	.	0	.	18	100	447	63
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	265	57	511	51	275	43	1150	25	1378	27	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21404	5	26545	5	49902	3	72353	3	11084	8	181288	2
	fir	16423	6	15000	6	22511	5	7905	8	1829	18	63668	3
	pine	2314	13	2814	11	725	32	5536	9	401	42	11791	6
	larch	265	35	1284	16	456	36	17209	6	6078	8	25291	4
	Arolla pine	0	.	0	.	70	75	2775	10	15	60	2861	10
	other conifers	409	38	1066	26	85	53	116	29	29	93	1705	19
	beech	23969	4	22248	4	16136	5	7661	8	6315	9	76329	2
	maple	3224	7	3184	9	3106	8	2765	9	442	18	12721	4
	ash	2934	10	6935	8	3070	11	1816	12	1173	16	15928	5
	oak	2393	13	4232	10	307	23	635	21	957	16	8524	7
	sweet chestnut	0	.	77	92	0	.	131	49	5352	10	5559	9
	other broadleaves	2235	15	3499	12	1152	12	3805	9	4698	7	15389	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	75570	2	86884	2	97519	2	122708	2	38372	3	421054	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. 1218035. Birmensdorf, Swiss Federal Research Institute WSL

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