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
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 2004/06
		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	1207	12	2211	9	2029	7	3888	5	492	15	9828	4
	fir	820	9	639	15	952	10	349	16	118	46	2879	6
	pine	50	26	54	38	44	40	633	13	5	73	786	11
	larch	2	76	44	29	15	63	598	10	273	16	933	8
	Arolla pine	0	.	0	.	5	100	164	24	0	100	170	24
	other conifers	42	33	68	34	20	38	67	24	0	100	197	16
	beech	1351	8	1338	8	1025	10	638	16	1061	11	5414	4
	maple	370	12	525	15	448	13	399	12	98	22	1840	6
	ash	306	17	567	16	266	15	291	13	279	20	1710	8
	oak	154	32	161	22	7	56	104	20	173	25	600	13
	sweet chestnut	0	.	11	71	0	.	13	72	804	15	828	14
	other broadleaves	424	12	576	12	417	12	1228	9	1317	9	3963	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4726	4	6196	4	5230	4	8374	3	4623	5	29147	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2622	9	4357	8	5407	6	8746	5	1108	12	22240	3
	fir	1563	9	1591	11	2122	9	774	14	144	27	6194	5
	pine	279	22	198	27	55	45	1212	12	50	47	1794	9
	larch	36	57	203	32	4	100	1314	10	622	14	2180	8
	Arolla pine	0	.	0	.	22	78	523	14	6	77	550	14
	other conifers	125	38	128	27	11	63	29	62	0	.	293	21
	beech	3818	7	3005	7	2445	8	1518	11	1337	13	12123	4
	maple	880	10	831	12	1020	12	674	13	139	29	3545	6
	ash	635	16	1002	13	930	14	606	23	275	26	3447	8
	oak	491	20	465	20	65	36	141	31	299	23	1461	11
	sweet chestnut	0	.	8	100	0	.	16	65	1102	13	1127	13
	other broadleaves	470	14	660	13	472	17	932	11	1499	10	4032	6
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	10920	4	12449	4	12553	4	16484	3	6581	5	58986	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3754	8	5196	8	8912	6	13524	4	1740	13	33126	3
	fir	2885	9	2214	10	3358	8	1076	13	245	33	9778	5
	pine	810	17	790	15	154	31	1556	12	171	61	3480	8
	larch	65	37	188	29	19	58	2503	9	1006	15	3781	7
	Arolla pine	0	.	0	.	21	100	549	15	0	.	571	15
	other conifers	61	57	94	48	0	.	5	100	0	.	160	35
	beech	6369	6	4468	7	3647	8	1501	11	956	14	16940	3
	maple	961	11	731	15	641	14	532	13	51	118	2916	7
	ash	691	15	1630	12	800	18	387	23	182	25	3689	8
	oak	530	19	808	18	39	57	136	40	222	25	1736	11
	sweet chestnut	0	.	16	73	0	.	39	51	813	13	869	13
	other broadleaves	580	19	674	16	199	33	531	16	874	13	2859	8
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	16705	3	16809	4	17791	4	22338	3	6261	6	79903	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5043	7	7044	7	11802	5	14996	4	1888	12	40773	3
	fir	3846	8	3149	8	4649	7	1369	12	286	24	13299	4
	pine	820	16	1264	16	243	46	1101	13	112	39	3540	8
	larch	76	62	348	26	90	49	3510	7	1050	13	5074	6
	Arolla pine	0	.	0	.	4	100	474	15	0	.	479	14
	other conifers	51	74	106	48	0	.	0	.	9	100	166	39
	beech	5992	5	5319	6	3907	8	1447	13	927	13	17592	3
	maple	669	13	500	17	495	17	306	18	39	79	2009	8
	ash	572	17	1638	12	531	18	422	36	60	63	3222	9
	oak	366	20	1081	14	43	50	139	34	172	39	1801	10
	sweet chestnut	0	.	15	100	0	.	7	71	546	20	567	20
	other broadleaves	262	35	482	19	108	38	184	28	329	16	1364	11
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	17697	3	20945	3	21872	3	23955	3	5416	6	89885	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4998	7	6488	8	11676	6	12507	5	1803	12	37471	3
	fir	3263	11	3845	10	5027	7	1591	13	338	27	14064	5
	pine	451	26	906	16	117	44	756	17	36	58	2266	10
	larch	33	59	373	30	130	48	3812	8	1095	11	5443	6
	Arolla pine	0	.	0	.	0	.	226	21	7	100	233	21
	other conifers	0	.	216	36	0	.	0	.	14	100	230	35
	beech	3564	8	4863	8	2606	10	1525	17	580	17	13137	5
	maple	156	27	250	31	197	29	94	37	6	100	703	16
	ash	309	24	1060	15	291	31	144	32	30	62	1833	11
	oak	256	30	1072	14	50	50	50	44	95	40	1522	12
	sweet chestnut	0	.	6	100	0	.	22	81	462	20	490	19
	other broadleaves	143	31	183	41	67	49	518	35	82	47	993	21
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	13173	4	19261	4	20159	4	21245	3	4549	6	78387	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2473	12	3706	10	6131	7	6774	7	1110	16	20194	4
	fir	1776	12	1984	13	2964	11	1031	19	158	36	7912	6
	pine	141	38	304	28	77	51	247	29	11	100	779	17
	larch	14	100	125	38	115	55	1882	11	552	19	2688	9
	Arolla pine	0	.	0	.	10	100	204	24	0	.	214	24
	other conifers	18	100	218	51	0	.	0	.	0	.	236	48
	beech	1257	17	1871	13	1319	16	447	31	303	26	5197	8
	maple	47	79	74	50	8	100	62	52	0	.	191	33
	ash	176	40	436	27	66	58	43	58	25	100	745	19
	oak	342	35	400	29	37	71	40	63	19	82	838	21
	sweet chestnut	0	.	0	.	0	.	18	71	198	26	216	25
	other broadleaves	96	46	50	59	0	.	56	59	38	59	241	28
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	6339	7	9168	6	10728	6	10804	5	2414	9	39452	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1021	17	1352	16	3008	11	4015	9	879	22	10275	6
	fir	940	20	919	18	1554	14	487	25	150	41	4050	9
	pine	0	.	41	71	0	.	0	.	0	.	41	71
	larch	24	100	25	100	25	100	1100	16	306	26	1480	13
	Arolla pine	0	.	0	.	0	.	144	30	0	.	144	30
	other conifers	28	100	57	71	40	100	0	.	0	.	125	51
	beech	330	27	1158	20	174	48	212	34	119	36	1993	14
	maple	0	.	35	100	10	100	30	72	0	.	76	56
	ash	47	100	166	42	65	71	17	100	0	.	294	33
	oak	43	71	378	32	112	53	41	72	0	.	574	25
	sweet chestnut	0	.	0	.	0	.	0	.	204	28	204	28
	other broadleaves	46	100	62	60	0	.	24	100	25	71	156	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2477	12	4193	10	4989	8	6070	7	1683	13	19412	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	535	44	317	35	884	25	1487	15	387	26	3610	12
	fir	540	27	172	41	404	29	221	39	113	58	1450	16
	pine	0	.	25	100	0	.	28	100	0	.	52	71
	larch	0	.	0	.	0	.	623	22	179	43	802	19
	Arolla pine	0	.	0	.	0	.	74	45	0	.	74	45
	other conifers	0	.	31	100	0	.	0	.	0	.	31	100
	beech	95	72	274	42	190	44	78	59	11	100	649	25
	maple	0	.	0	.	0	.	12	100	14	100	27	71
	ash	0	.	68	75	0	.	0	.	0	.	68	75
	oak	23	100	127	46	0	.	0	.	0	.	150	42
	sweet chestnut	0	.	0	.	0	.	11	100	183	31	194	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1194	25	1014	19	1479	18	2535	11	887	18	7108	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	78	71	33	100	408	31	532	24	168	43	1219	17
	fir	39	100	72	71	166	50	0	.	105	58	381	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	132	41	34	74	166	36
	Arolla pine	0	.	0	.	0	.	35	71	0	.	35	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	50	100	179	51	0	.	0	.	43	72	271	40
	maple	0	.	0	.	18	100	0	.	0	.	18	100
	ash	0	.	35	100	0	.	0	.	0	.	35	100
	oak	35	100	99	77	0	.	0	.	0	.	134	63
	sweet chestnut	0	.	0	.	0	.	0	.	82	61	82	61
	other broadleaves	24	100	106	74	0	.	48	100	17	100	195	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	225	42	524	30	592	26	747	20	449	26	2538	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	77	71	75	75	326	31	156	61	671	24
	fir	0	.	55	100	115	71	87	71	29	100	287	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	100	51	139	62	239	42
	Arolla pine	0	.	0	.	0	.	53	58	0	.	53	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	432	100	432	100
	maple	0	.	0	.	0	.	73	76	0	.	73	76
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	31	100	0	.	0	.	0	.	31	100
	sweet chestnut	0	.	0	.	0	.	0	.	606	41	606	41
	other broadleaves	47	100	0	.	0	.	0	.	0	.	47	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	83	71	163	51	190	52	639	22	1363	38	2439	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21767	5	30783	5	50332	4	66793	3	9732	8	179406	2
	fir	15673	6	14640	6	21312	5	6985	9	1686	18	60295	3
	pine	2551	12	3581	10	690	30	5532	9	385	41	12739	6
	larch	250	33	1306	17	399	37	15575	5	5256	8	22785	4
	Arolla pine	0	.	0	.	64	79	2446	11	13	63	2522	11
	other conifers	324	37	917	26	71	58	102	30	23	98	1437	19
	beech	22825	4	22475	4	15313	6	7366	9	5769	11	73749	2
	maple	3083	7	2946	8	2838	8	2184	9	348	24	11398	4
	ash	2734	11	6602	8	2948	11	1909	17	851	17	15045	5
	oak	2241	14	4623	10	352	23	652	22	981	18	8849	7
	sweet chestnut	0	.	56	82	0	.	127	44	5001	10	5184	9
	other broadleaves	2090	15	2794	10	1264	12	3521	10	4181	7	13850	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	73538	2	90722	2	95582	2	113191	2	34225	3	407258	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. 1217969. Birmensdorf, Swiss Federal Research Institute WSL

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