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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2802	10	2270	10	998	13	1040	14	2014	11	610	16	9734	5
	fir	1699	9	803	17	372	21	90	36	18	39	66	35	3046	7
	pine	18	182	37	61	-2	1007	220	33	247	26	14	65	534	20
	larch	128	49	46	40	15	57	393	24	147	21	263	24	993	13
	Arolla pine	0	.	0	.	0	.	117	57	144	28	3	85	263	30
	other conifers	133	26	15	68	40	34	9	56	34	34	1	100	233	17
	beech	2634	7	819	16	585	19	113	32	58	31	934	14	5144	5
	maple	1004	13	445	18	228	21	173	25	53	34	176	31	2078	8
	ash	600	16	173	28	194	34	112	31	64	34	215	24	1358	10
	oak	227	28	15	79	4	71	61	35	3	100	94	42	405	20
	sweet chestnut	0	.	0	.	0	.	12	100	5	100	668	20	684	20
	other broadleaves	1019	10	502	14	341	19	466	16	449	22	1181	12	3958	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10264	4	5124	7	2776	9	2806	9	3236	8	4224	6	28430	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5187	9	4415	9	2921	12	2316	15	4071	10	1339	15	20250	4
	fir	3525	10	1787	14	793	23	165	36	32	50	132	35	6435	7
	pine	411	28	103	71	84	53	641	23	455	26	5	100	1699	14
	larch	178	40	77	58	64	67	771	17	429	27	949	18	2469	11
	Arolla pine	0	.	0	.	0	.	187	29	384	23	5	100	576	18
	other conifers	136	27	38	62	16	71	4	100	8	73	0	.	202	23
	beech	5083	7	2056	12	1337	15	274	38	198	34	1733	17	10682	5
	maple	1912	11	802	17	848	18	170	30	89	41	91	39	3912	7
	ash	880	15	488	29	458	24	137	39	114	74	315	33	2391	11
	oak	724	21	63	42	7	100	98	39	6	100	303	24	1201	15
	sweet chestnut	0	.	0	.	0	.	16	71	5	100	1410	16	1431	15
	other broadleaves	1175	12	405	17	452	25	428	24	392	27	1679	16	4532	8
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	19211	4	10236	6	6980	8	5207	9	6183	8	7961	6	55779	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7068	8	7458	8	4991	10	3033	12	5572	9	2221	18	30344	4
	fir	4340	9	2447	13	1214	18	312	34	133	36	372	34	8817	7
	pine	1016	18	187	45	171	43	547	22	528	27	42	106	2491	12
	larch	120	48	82	52	81	77	1290	18	947	16	1320	17	3839	10
	Arolla pine	0	.	12	100	0	.	204	31	438	23	0	.	654	18
	other conifers	139	39	5	100	-25	100	0	0	0	.	0	.	119	50
	beech	8591	6	2397	12	2253	13	247	34	234	29	1459	15	15180	5
	maple	1865	11	895	18	971	17	49	47	108	43	159	45	4048	8
	ash	1855	12	728	32	529	24	144	43	62	49	105	38	3423	10
	oak	768	18	34	59	26	59	74	41	6	100	267	26	1175	14
	sweet chestnut	0	.	0	.	0	.	52	60	0	.	904	19	956	19
	other broadleaves	904	15	118	46	213	24	137	45	93	34	1246	18	2710	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	26666	4	14363	6	10424	7	6090	9	8120	7	8095	8	73757	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8671	7	9919	7	5722	10	3260	12	6958	9	2691	16	37221	3
	fir	6220	8	2693	11	1977	14	340	34	185	35	300	32	11716	5
	pine	1289	14	265	57	88	44	347	26	627	26	63	57	2681	11
	larch	258	32	70	84	42	75	1924	17	1612	15	1237	14	5143	9
	Arolla pine	0	.	0	.	0	.	157	33	320	24	0	.	476	19
	other conifers	175	39	0	.	0	.	0	.	0	.	16	100	191	36
	beech	9921	6	2899	14	2981	13	89	70	360	36	975	16	17225	5
	maple	1202	12	407	22	566	18	84	74	65	54	52	49	2376	9
	ash	1962	13	345	27	556	27	54	85	0	0	106	61	3022	10
	oak	1312	16	123	53	49	61	20	76	22	100	220	31	1745	13
	sweet chestnut	0	.	6	100	0	.	10	100	5	100	838	23	859	22
	other broadleaves	862	17	168	38	62	51	66	46	57	73	721	20	1936	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	31871	3	16893	6	12043	7	6351	9	10212	7	7221	7	84590	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7503	8	8565	8	6466	9	2729	13	5653	10	2453	18	33369	4
	fir	6706	8	4141	11	2387	17	452	39	320	33	612	28	14617	6
	pine	1117	18	128	52	120	57	145	42	522	27	27	100	2060	13
	larch	337	29	104	53	102	71	2081	15	1696	16	1233	15	5553	8
	Arolla pine	0	.	0	.	0	.	92	54	246	27	0	.	338	25
	other conifers	408	41	0	.	0	.	0	.	0	.	28	100	436	39
	beech	9220	6	2546	15	1764	17	101	65	175	47	697	21	14502	5
	maple	725	18	302	29	365	27	0	.	118	42	0	.	1509	13
	ash	1852	16	149	50	360	30	15	100	0	.	88	64	2464	13
	oak	1277	16	0	.	19	100	22	100	70	76	99	46	1488	15
	sweet chestnut	0	.	0	.	0	.	12	100	8	100	537	27	557	26
	other broadleaves	600	28	166	64	145	39	54	60	42	71	104	42	1110	19
	indeterminable	0	.	0	0	0	.	0	0	0	.	0	.	0	0
	total	29744	3	16100	6	11727	7	5704	10	8852	8	5878	10	78004	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5603	9	5636	10	4666	12	1502	19	3266	13	1425	17	22098	5
	fir	4538	11	2982	15	1164	20	99	75	290	40	138	50	9211	8
	pine	325	34	81	71	41	100	17	100	219	35	14	100	698	22
	larch	167	45	23	100	27	100	1542	19	1343	20	738	21	3841	11
	Arolla pine	0	.	0	.	0	.	39	71	67	59	0	.	107	45
	other conifers	341	46	0	.	0	.	0	.	0	.	36	100	377	43
	beech	4559	10	843	27	846	26	67	74	167	42	500	29	6981	8
	maple	291	37	129	46	131	70	0	.	0	.	0	.	551	28
	ash	802	21	104	58	181	52	0	.	0	.	55	100	1143	19
	oak	681	28	0	0	0	.	0	.	32	100	34	100	747	26
	sweet chestnut	0	.	0	.	0	.	16	100	0	.	178	33	194	31
	other broadleaves	263	41	174	81	91	58	26	100	74	78	47	73	674	29
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	17571	5	9973	8	7148	10	3307	13	5457	10	3164	10	46621	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2783	15	2976	13	2847	17	1011	28	1622	18	1016	29	12255	7
	fir	2765	13	1802	19	1143	26	184	61	267	46	195	50	6357	10
	pine	202	45	0	.	0	.	0	.	62	71	0	.	264	38
	larch	41	100	62	71	135	58	789	26	564	26	434	28	2026	15
	Arolla pine	0	.	0	.	0	.	87	50	65	58	0	.	151	38
	other conifers	277	60	0	.	0	.	0	.	0	.	0	.	277	60
	beech	1825	18	648	29	306	56	30	100	118	70	170	41	3097	14
	maple	106	58	46	72	0	.	0	.	0	.	0	.	152	46
	ash	174	51	43	100	92	72	0	.	33	100	30	100	371	34
	oak	447	34	0	.	0	.	0	.	10	100	0	.	457	34
	sweet chestnut	0	.	0	.	0	.	18	100	0	.	168	38	186	36
	other broadleaves	216	41	38	100	0	.	0	.	0	.	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8836	8	5615	10	4523	13	2118	18	2741	14	2040	17	25874	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1105	25	923	25	808	26	278	43	913	27	440	39	4469	12
	fir	1438	22	732	33	455	42	0	.	152	72	50	100	2828	16
	pine	49	100	0	.	0	.	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	451	39	303	44	447	39	1202	23
	Arolla pine	0	.	0	.	0	.	69	75	70	71	0	.	138	51
	other conifers	296	52	81	100	0	.	0	.	0	.	0	.	376	46
	beech	217	50	44	100	162	59	0	0	0	.	171	45	593	29
	maple	0	.	0	.	27	100	0	.	0	.	0	.	27	100
	ash	142	71	0	.	140	71	0	.	0	.	0	.	282	50
	oak	215	51	0	.	83	71	0	.	0	.	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	70	100	0	.	0	.	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3531	14	1780	19	1674	19	798	27	1438	21	1263	23	10484	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	295	45	524	34	210	51	124	61	317	41	215	58	1685	19
	fir	339	45	61	100	204	58	0	.	0	.	204	58	807	29
	pine	0	.	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	228	45	121	72	38	100	387	36
	Arolla pine	0	.	0	.	0	.	57	71	0	.	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	281	53	0	.	55	100	0	.	0	.	0	.	337	47
	maple	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	ash	119	71	0	.	0	.	0	.	0	.	0	.	119	71
	oak	89	71	0	.	0	.	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	other broadleaves	55	100	0	.	0	.	0	.	0	.	0	.	55	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1179	23	585	32	494	34	439	31	495	34	601	32	3792	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	236	58	214	51	269	45	64	100	306	50	184	72	1274	24
	fir	174	72	133	100	0	.	0	.	0	.	0	.	307	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	107	71	129	100	236	63
	Arolla pine	0	.	0	.	0	.	39	100	36	100	0	.	75	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	194	100	0	.	0	.	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	411	55	411	55
	other broadleaves	223	59	0	.	0	.	0	.	0	.	0	.	223	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	826	36	347	50	269	45	103	73	448	39	725	40	2719	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41253	5	42901	5	29898	7	15359	9	30693	7	12596	12	172699	2
	fir	31742	5	17581	8	9710	11	1642	26	1397	24	2068	24	64140	4
	pine	4427	11	801	41	503	34	1918	18	2717	17	165	49	10531	8
	larch	1230	18	463	33	466	36	9470	12	7270	11	6789	11	25688	6
	Arolla pine	0	.	12	100	0	.	1047	22	1768	18	9	94	2836	13
	other conifers	1904	29	138	64	32	81	13	58	42	35	82	98	2211	25
	beech	42332	4	12251	10	10289	10	920	30	1310	27	6638	11	73741	3
	maple	7106	7	3026	11	3159	12	475	26	432	30	479	25	14678	5
	ash	8385	9	2030	21	2509	16	463	31	274	40	913	30	14573	7
	oak	5934	12	236	37	187	43	276	33	149	71	1017	20	7799	10
	sweet chestnut	0	.	6	100	0	.	165	51	23	82	5412	13	5605	12
	other broadleaves	5386	9	1571	22	1304	16	1175	19	1108	21	5004	11	15548	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	149699	2	81017	4	58057	6	32923	7	47182	5	41172	4	410050	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. 1218059. Birmensdorf, Swiss Federal Research Institute WSL

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