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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NFI5
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	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	.
	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	17789	8	19588	8	9588	11	9976	11	16463	8	5766	15	79170	4
	fir	12379	8	6444	12	2897	15	629	27	191	37	698	27	23238	6
	pine	532	25	582	43	291	65	2652	26	3330	24	181	60	7569	15
	larch	865	49	477	38	281	50	3274	14	1877	18	3281	17	10055	9
	Arolla pine	0	.	0	.	0	.	752	31	1745	27	49	74	2546	20
	other conifers	1062	27	241	63	380	40	173	56	893	38	20	100	2768	18
	beech	19735	6	6283	11	4549	14	1469	27	641	25	6441	14	39119	5
	maple	7286	10	3458	15	1955	19	1542	26	373	35	1353	28	15967	7
	ash	4247	12	1768	17	1211	24	926	30	467	35	1660	22	10279	8
	oak	2167	25	177	63	53	74	805	35	15	100	1184	37	4403	17
	sweet chestnut	0	.	0	.	0	.	131	77	18	100	7956	15	8105	15
	other broadleaves	8972	8	5428	13	3549	19	4674	14	4039	20	12384	11	39046	5
	indeterminable	191	35	195	30	104	41	90	45	181	37	110	41	871	15
	total	75223	3	44643	5	24857	8	27095	7	30234	7	41082	6	243136	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10056	8	11809	8	7094	10	5649	13	10227	8	3623	13	48458	4
	fir	7008	8	3861	11	1468	16	341	30	191	40	338	32	13208	6
	pine	946	22	347	64	265	42	2241	20	1267	22	52	58	5118	12
	larch	362	37	138	50	120	62	2335	16	1410	21	2796	15	7161	9
	Arolla pine	0	.	0	.	0	.	614	26	1284	22	16	100	1914	17
	other conifers	315	26	104	71	54	58	17	100	35	71	0	.	525	22
	beech	10473	6	4179	11	3019	13	674	36	561	33	3994	16	22899	5
	maple	4030	9	2006	14	1630	16	457	28	194	45	306	31	8625	7
	ash	1845	12	1048	21	1110	20	417	38	278	66	710	31	5408	9
	oak	1462	19	164	40	93	65	288	38	15	100	872	22	2894	13
	sweet chestnut	0	.	0	.	0	.	37	71	15	100	4496	14	4549	14
	other broadleaves	2996	11	1353	16	1436	23	1384	19	1548	24	5085	12	13801	7
	indeterminable	71	50	105	41	0	.	36	71	75	62	18	100	305	26
	total	39565	3	25115	5	16288	7	14491	8	17100	7	22304	5	134863	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6408	7	8243	8	4998	9	3697	11	6059	8	2284	14	31690	3
	fir	3728	9	2317	12	1045	17	312	35	144	31	283	31	7830	6
	pine	1148	17	270	44	235	47	832	20	679	23	147	51	3311	11
	larch	111	39	127	49	74	73	1627	15	1186	16	1622	15	4748	8
	Arolla pine	0	.	10	100	0	.	342	31	648	24	0	.	1000	19
	other conifers	138	38	7	100	15	100	17	100	0	.	0	.	177	33
	beech	7573	6	2352	11	2234	13	291	32	324	27	1762	15	14535	4
	maple	1734	11	973	17	949	18	64	48	118	42	187	42	4024	8
	ash	1669	14	662	30	550	22	190	38	124	46	169	32	3364	10
	oak	771	17	37	63	23	58	114	43	7	100	313	27	1266	13
	sweet chestnut	0	.	0	.	0	.	87	62	0	.	1646	18	1733	17
	other broadleaves	1046	14	191	31	283	25	142	30	163	33	1823	16	3648	10
	indeterminable	51	58	85	39	21	58	7	100	41	61	19	100	224	25
	total	24375	3	15275	5	10428	7	7722	8	9493	7	10256	6	77548	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4509	6	5680	7	3088	9	2046	11	3998	8	1575	13	20895	3
	fir	2918	7	1378	10	942	14	181	34	95	33	184	29	5699	5
	pine	733	13	162	50	66	46	310	27	385	23	41	52	1697	10
	larch	118	31	36	68	43	70	1277	15	965	14	912	14	3350	8
	Arolla pine	0	.	0	.	0	.	140	32	262	22	0	.	402	18
	other conifers	82	37	0	.	0	.	0	.	0	.	8	100	90	35
	beech	4826	5	1424	13	1503	12	77	55	208	33	679	15	8716	4
	maple	646	12	281	19	347	17	56	60	41	55	44	47	1414	8
	ash	993	12	171	25	280	23	27	71	7	100	66	44	1544	9
	oak	685	15	61	48	32	60	21	75	12	100	173	29	985	12
	sweet chestnut	0	.	7	100	0	.	16	71	6	100	721	18	750	18
	other broadleaves	542	18	130	35	43	47	67	37	56	57	524	18	1362	11
	indeterminable	15	71	41	52	21	75	7	100	21	58	0	.	105	30
	total	16067	3	9371	5	6365	7	4225	8	6056	6	4926	6	47010	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2526	7	3067	7	2220	9	1145	12	2194	9	1025	20	12178	4
	fir	1982	8	1271	11	661	17	167	37	105	32	197	28	4383	6
	pine	386	17	56	52	46	53	78	46	220	27	21	74	808	13
	larch	98	28	35	53	27	71	843	14	657	16	580	14	2240	8
	Arolla pine	0	.	0	.	0	.	63	51	149	27	0	.	212	24
	other conifers	116	38	0	.	0	.	0	.	0	.	8	100	124	36
	beech	2867	6	798	15	636	16	34	63	65	47	317	20	4717	5
	maple	266	17	109	28	144	27	0	.	52	41	0	.	571	12
	ash	529	15	59	46	115	28	7	100	0	.	40	63	751	12
	oak	411	16	0	.	6	100	8	100	27	81	57	41	509	14
	sweet chestnut	0	.	0	.	0	.	8	100	6	100	337	24	352	23
	other broadleaves	216	25	63	56	63	40	23	57	25	71	57	42	447	17
	indeterminable	0	.	19	58	0	.	14	71	0	.	0	.	34	45
	total	9398	3	5477	6	3918	7	2392	9	3500	7	2639	10	27325	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1259	9	1422	10	1118	12	444	17	800	13	423	16	5466	5
	fir	1024	10	663	14	240	20	26	60	59	39	37	45	2047	7
	pine	75	33	17	72	7	100	7	100	65	37	7	100	179	21
	larch	34	45	7	100	6	100	452	18	357	19	222	21	1078	11
	Arolla pine	0	.	0	.	0	.	14	71	22	58	0	.	36	45
	other conifers	61	46	0	.	0	.	0	.	0	.	8	100	69	42
	beech	980	10	191	29	188	24	20	73	41	41	151	28	1570	8
	maple	63	37	41	47	35	63	0	.	0	.	0	.	140	27
	ash	172	22	24	58	41	52	0	.	0	.	10	100	247	19
	oak	142	28	7	100	0	.	0	.	6	100	15	71	170	25
	sweet chestnut	0	.	0	.	0	.	16	71	0	.	98	27	114	25
	other broadleaves	57	39	38	69	22	58	7	100	18	72	15	71	158	26
	indeterminable	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	total	3866	5	2410	8	1658	10	993	12	1367	10	987	9	11281	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	476	14	632	12	487	16	200	25	323	17	217	25	2335	7
	fir	496	13	294	18	189	26	37	60	34	45	30	49	1080	9
	pine	34	45	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	14	71	22	58	180	26	117	26	93	27	433	15
	Arolla pine	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	41	58	0	.	0	.	0	.	0	.	0	.	41	58
	beech	313	17	119	27	53	50	6	100	25	79	44	41	560	13
	maple	23	58	13	71	0	.	0	.	0	.	0	.	36	45
	ash	32	50	8	100	13	71	0	.	6	100	6	100	65	34
	oak	69	34	0	.	0	.	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	104	33	112	31
	other broadleaves	43	41	6	100	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1534	7	1086	9	763	13	460	17	548	13	502	14	4892	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	150	24	131	25	138	25	43	41	141	26	88	33	691	11
	fir	183	22	96	32	51	42	0	.	15	71	7	100	352	16
	pine	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	81	37	63	40	95	37	239	22
	Arolla pine	0	.	0	.	0	.	28	71	15	71	0	.	43	52
	other conifers	34	53	7	100	0	.	0	.	0	.	0	.	41	47
	beech	28	50	7	100	23	58	7	100	0	.	37	45	102	27
	maple	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	16	71	0	.	17	72	0	.	0	.	0	.	33	51
	oak	29	50	0	.	18	71	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	53	47	53	47
	other broadleaves	8	100	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	454	13	241	19	256	18	160	26	233	20	280	20	1624	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	35	45	71	34	36	45	22	58	49	43	25	59	238	18
	fir	42	41	6	100	21	58	0	.	0	.	25	58	95	28
	pine	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	36	45	18	72	7	100	61	36
	Arolla pine	0	.	0	.	0	.	22	58	0	.	0	.	22	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	32	52	0	.	7	100	0	.	0	.	0	.	39	46
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	17	71	0	.	0	.	0	.	0	.	0	.	17	71
	oak	14	71	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	45	53	53	48
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	147	22	77	32	70	32	87	29	73	35	103	32	558	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	28	50	29	50	36	45	7	100	30	50	18	72	147	22
	fir	15	71	7	100	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	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	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	27	61	0	.	0	.	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	86	34	36	45	36	45	14	71	51	38	83	39	306	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	43236	5	50670	6	28803	8	23230	9	40283	7	15045	11	201267	3
	fir	29775	5	16338	8	7513	11	1693	24	835	26	1800	23	57954	4
	pine	3860	13	1435	40	910	36	6120	19	5968	19	450	46	18744	10
	larch	1594	32	835	30	571	45	10107	11	6665	11	9621	11	29394	6
	Arolla pine	0	.	10	100	0	.	2010	22	4153	20	65	79	6237	15
	other conifers	1849	19	359	62	449	36	208	56	927	37	44	70	3835	15
	beech	46827	4	15352	9	12212	10	2579	27	1863	23	13424	12	92257	3
	maple	14048	7	6882	11	5074	12	2119	24	778	29	1890	25	30791	5
	ash	9520	8	3741	16	3338	15	1567	27	882	42	2661	22	21709	6
	oak	5766	15	447	35	226	39	1236	31	91	72	2614	24	10380	11
	sweet chestnut	0	.	7	100	0	.	311	67	45	72	15506	12	15870	12
	other broadleaves	13913	7	7209	12	5396	17	6297	13	5848	20	19895	9	58559	5
	indeterminable	328	26	445	20	146	33	162	33	319	27	146	40	1546	11
	total	170716	2	103731	4	64638	6	57640	6	68657	5	83161	4	548544	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. 1358386. Birmensdorf, Swiss Federal Research Institute WSL

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