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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NFI3
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/07
		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	20453	8	13267	9	7745	12	6629	13	12173	10	3062	16	63328	4
	fir	10508	8	5511	11	2427	19	573	31	353	32	499	34	19872	6
	pine	773	38	408	60	70	61	2559	24	2015	27	49	100	5875	15
	larch	252	54	340	43	152	70	2421	18	847	23	1922	23	5934	11
	Arolla pine	0	.	0	.	0	.	557	32	777	28	16	100	1350	21
	other conifers	914	25	17	100	193	45	95	55	483	44	0	.	1703	19
	beech	17597	6	6157	12	4793	13	1177	33	597	29	5920	15	36241	5
	maple	5421	11	2603	19	1492	18	696	28	268	39	490	31	10970	8
	ash	3838	13	1104	20	1300	23	440	42	525	43	1205	29	8413	9
	oak	2010	25	177	55	21	100	604	35	15	100	1304	36	4130	17
	sweet chestnut	66	100	0	.	0	.	0	0	0	.	6351	17	6418	17
	other broadleaves	6365	10	3123	15	2897	19	3572	19	3076	27	8742	14	27775	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	68198	4	32707	6	21091	9	19322	8	21130	8	29560	7	192008	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12355	8	10158	9	6531	13	4600	14	7447	10	2420	15	43511	4
	fir	5901	9	3066	12	1355	17	350	36	125	56	375	38	11172	6
	pine	1044	23	244	81	208	52	1720	20	1132	26	102	62	4450	13
	larch	225	40	95	61	78	75	1677	18	710	24	1453	18	4237	10
	Arolla pine	0	.	0	.	0	.	507	32	847	24	16	100	1370	19
	other conifers	360	28	17	100	33	71	39	71	35	71	0	.	484	23
	beech	13186	6	4003	12	3352	14	724	32	544	32	3554	17	25362	5
	maple	3156	10	1747	16	1545	17	155	40	158	40	193	37	6954	7
	ash	2504	13	1059	25	1740	25	294	43	257	63	389	35	6242	10
	oak	2059	18	143	43	54	74	164	59	15	100	671	28	3107	14
	sweet chestnut	0	.	0	.	0	.	37	100	15	100	3747	15	3799	15
	other broadleaves	2319	12	877	22	864	26	733	24	635	34	3497	12	8925	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	43108	3	21409	6	15760	8	11000	9	11920	8	16416	6	119613	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7284	7	6506	8	4454	10	2674	12	4859	9	1944	16	27722	4
	fir	3872	9	1759	12	1008	15	175	48	131	42	195	35	7139	6
	pine	1335	15	160	47	93	58	703	21	806	25	68	60	3165	11
	larch	183	31	31	80	32	81	1140	19	837	21	1028	21	3250	11
	Arolla pine	0	.	10	100	0	.	241	32	386	26	0	.	637	20
	other conifers	120	50	0	.	0	.	0	.	0	.	0	.	120	50
	beech	8696	6	2359	17	2022	13	136	51	300	28	1099	17	14612	5
	maple	1312	12	622	19	684	19	78	56	58	49	59	56	2813	9
	ash	2073	13	516	35	627	22	127	84	40	56	119	42	3501	10
	oak	1009	16	57	56	42	55	26	78	0	.	199	35	1334	13
	sweet chestnut	0	.	0	.	0	.	29	73	6	100	1052	18	1087	18
	other broadleaves	1039	16	257	33	199	30	188	36	94	43	880	18	2657	10
	indeterminable	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	26924	3	12275	6	9161	7	5517	9	7517	7	6643	7	68037	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5426	6	4675	7	2817	9	1717	11	3366	10	1225	14	19227	3
	fir	3175	7	1290	11	817	15	182	37	81	37	176	31	5720	5
	pine	1073	13	162	57	47	47	178	37	375	24	56	66	1890	11
	larch	125	29	43	59	0	0	1048	16	820	15	567	16	2603	9
	Arolla pine	0	.	0	0	0	.	105	42	206	23	0	.	311	21
	other conifers	116	42	0	.	0	.	0	.	0	.	8	100	124	40
	beech	5113	5	1277	13	1286	14	49	60	207	33	513	18	8445	4
	maple	626	15	230	21	289	20	14	71	30	61	8	100	1197	10
	ash	1073	12	133	30	312	39	21	76	7	100	37	66	1583	12
	oak	649	15	34	73	7	100	27	77	33	71	120	51	869	14
	sweet chestnut	0	.	7	100	0	.	7	100	0	.	436	22	451	21
	other broadleaves	435	19	44	43	66	48	31	61	35	53	246	22	856	13
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	17811	3	7896	5	5641	7	3379	9	5159	7	3391	7	43276	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3450	7	2866	8	1995	10	855	14	1604	10	711	16	11481	4
	fir	1848	8	999	11	501	15	72	45	116	34	104	33	3640	6
	pine	382	15	44	54	16	71	69	42	152	29	7	100	669	12
	larch	110	36	56	53	48	48	668	16	623	15	384	16	1889	9
	Arolla pine	0	.	0	.	0	.	7	100	100	30	0	.	106	29
	other conifers	76	41	0	.	0	.	0	.	0	.	8	100	83	39
	beech	2410	7	527	16	503	17	50	61	80	36	243	20	3813	6
	maple	139	28	46	42	65	36	0	.	20	73	0	.	270	19
	ash	474	15	44	47	78	37	0	.	0	.	0	.	596	13
	oak	320	19	0	.	6	100	0	.	13	71	33	59	372	17
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	206	26	212	26
	other broadleaves	88	40	85	55	89	49	30	61	7	100	22	58	322	24
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	9296	4	4667	6	3301	8	1751	10	2720	8	1718	8	23453	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1252	10	1010	11	761	13	282	22	606	15	320	18	4231	5
	fir	748	12	444	18	252	23	29	61	14	71	29	50	1517	9
	pine	69	32	10	100	7	100	0	.	64	37	0	.	151	23
	larch	20	58	21	74	13	71	341	21	161	25	151	25	707	13
	Arolla pine	0	.	0	.	0	.	7	100	29	50	0	.	36	45
	other conifers	70	49	0	.	0	.	0	.	0	.	0	.	70	49
	beech	509	13	231	23	127	31	0	.	14	71	85	30	967	10
	maple	39	45	23	59	0	.	0	.	0	.	0	.	63	36
	ash	120	30	7	100	17	73	0	.	6	100	11	100	162	25
	oak	141	32	0	.	0	.	0	.	7	100	0	.	148	31
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	75	34	83	33
	other broadleaves	29	50	20	58	0	.	0	.	0	.	8	100	56	35
	indeterminable	0	.	0	0	0	.	0	.	0	.	0	.	0	0
	total	2998	6	1768	9	1178	11	667	15	900	12	680	11	8190	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	370	16	387	15	446	17	117	28	216	20	161	27	1696	8
	fir	307	17	159	22	68	37	7	100	35	45	22	57	598	12
	pine	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	larch	7	100	0	.	16	71	166	26	100	30	49	38	338	17
	Arolla pine	0	.	0	.	0	.	42	47	21	58	0	.	64	37
	other conifers	8	100	7	100	0	.	0	.	0	.	0	.	15	71
	beech	182	26	43	47	30	60	6	100	18	74	44	41	324	18
	maple	0	.	0	.	10	100	0	.	0	.	0	.	10	100
	ash	45	46	7	100	14	71	0	.	0	.	0	.	67	36
	oak	52	38	0	.	16	71	0	.	0	.	0	.	67	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	46	46	46	46
	other broadleaves	14	71	0	.	0	.	0	.	0	.	7	100	21	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	991	10	603	12	600	14	338	18	391	15	330	17	3253	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	79	33	83	30	109	44	62	51	70	35	61	39	464	16
	fir	79	30	51	38	27	50	0	.	7	100	18	71	182	20
	pine	7	100	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	79	35	35	53	29	61	143	26
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	41	45	28	61	24	58	7	100	0	.	7	100	108	28
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	10	100	0	.	0	.	0	.	0	.	0	.	10	100
	oak	39	46	0	.	0	.	0	.	0	.	0	.	39	46
	sweet chestnut	0	.	0	.	0	.	8	100	0	.	45	46	53	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	256	18	162	22	166	31	164	27	127	26	160	24	1034	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	21	58	42	46	0	0	21	58	11	100	95	30
	fir	21	58	0	.	0	.	0	.	0	.	18	72	39	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	14	71	7	100	0	.	21	58
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	22	58	0	.	0	.	0	.	0	.	0	.	22	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	7	100	0	.	0	.	0	.	0	.	0	.	7	100
	oak	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	37	59	37	59
	other broadleaves	28	79	0	.	0	.	0	.	0	.	0	.	28	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	94	35	21	58	42	46	21	58	27	50	66	42	272	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	58	7	100	15	71	7	100	15	71	8	100	74	32
	fir	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	7	100	14	100	22	74
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	53	50	53	50
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	21	58	14	71	15	71	14	71	30	50	74	42	170	24
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	50691	5	38979	6	24917	8	16943	11	30377	7	9924	11	171831	3
	fir	26459	6	13286	8	6454	12	1389	27	862	25	1436	26	49886	4
	pine	4690	12	1028	47	440	45	5229	19	4551	19	282	55	16220	9
	larch	922	22	585	33	339	48	7555	12	4146	13	5597	15	19143	7
	Arolla pine	0	.	10	100	0	.	1488	25	2380	20	32	100	3910	16
	other conifers	1663	19	42	61	226	42	134	58	518	43	16	100	2600	15
	beech	47756	4	14626	10	12138	10	2149	30	1760	24	11465	13	89894	3
	maple	10694	7	5271	14	4091	13	942	27	535	29	750	27	22282	5
	ash	10145	8	2871	20	4087	18	882	43	834	46	1761	27	20580	7
	oak	6294	14	411	36	147	41	821	34	84	74	2326	26	10082	11
	sweet chestnut	66	100	7	100	0	.	89	66	28	100	12047	13	12238	13
	other broadleaves	10315	8	4405	14	4116	18	4554	18	3848	25	13402	11	40639	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	169695	2	81522	4	56955	6	42174	7	49921	6	59038	4	459304	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. 1326979. Birmensdorf, Swiss Federal Research Institute WSL

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