Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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	2939	7	2106	7	1158	10	947	10	1972	8	626	15	9748	4
	fir	1770	8	847	10	447	16	118	28	25	32	96	33	3303	6
	pine	53	50	59	55	24	36	272	23	308	16	7	66	723	13
	larch	47	42	38	39	19	43	394	14	259	20	260	16	1018	9
	Arolla pine	0	.	1	100	4	80	82	31	140	24	2	65	229	19
	other conifers	65	45	12	54	30	45	9	50	40	32	2	73	157	22
	beech	2573	6	794	12	558	12	145	34	97	26	1024	11	5192	4
	maple	1029	9	449	11	310	17	126	19	50	42	157	19	2120	6
	ash	802	12	183	21	233	19	119	39	78	32	242	19	1657	8
	oak	235	18	24	50	7	58	72	26	6	55	125	25	468	12
	sweet chestnut	5	62	0	.	0	.	15	76	0	.	667	16	687	15
	other broadleaves	1000	8	454	12	359	18	502	13	499	16	1314	9	4127	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	10518	3	4967	5	3148	7	2803	7	3473	6	4522	5	29431	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6323	6	4855	7	2968	9	2418	10	4070	7	1163	12	21798	3
	fir	2870	7	1608	10	744	15	296	27	109	26	159	28	5787	5
	pine	392	20	90	40	123	32	578	18	432	18	47	47	1662	10
	larch	158	27	30	60	44	48	853	12	533	15	753	14	2370	7
	Arolla pine	0	.	10	100	7	100	273	19	273	20	2	100	566	13
	other conifers	201	23	36	48	9	72	2	100	23	40	0	.	271	19
	beech	6014	5	1864	10	1448	11	348	28	204	30	1547	12	11425	4
	maple	1776	9	953	12	735	15	133	28	106	45	127	28	3830	6
	ash	1444	10	474	19	589	17	151	27	70	37	405	25	3132	7
	oak	861	14	66	39	5	100	68	29	8	59	242	23	1250	11
	sweet chestnut	7	100	0	.	0	.	15	64	3	100	1253	12	1278	12
	other broadleaves	1209	10	401	17	320	19	465	16	239	25	1498	10	4133	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	21256	3	10387	5	6993	6	5600	6	6068	6	7197	5	57502	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8619	6	8201	6	4986	8	3293	9	5876	7	2005	12	32979	3
	fir	4831	7	2400	9	1325	12	236	26	139	34	250	31	9180	5
	pine	1261	12	163	33	196	37	571	16	683	19	166	59	3041	8
	larch	343	23	29	59	124	35	1452	11	919	14	1130	12	3997	7
	Arolla pine	0	.	28	84	16	83	255	19	368	22	5	100	671	14
	other conifers	208	34	3	100	-4	365	0	.	0	.	0	.	208	35
	beech	9909	5	2617	10	2132	10	180	24	235	25	1211	12	16286	4
	maple	1716	9	708	13	659	14	39	51	63	56	94	36	3279	7
	ash	2259	9	528	23	547	17	81	37	46	44	278	24	3741	7
	oak	1044	12	70	44	16	153	42	52	20	86	290	20	1481	10
	sweet chestnut	16	82	0	.	0	.	23	64	0	.	949	14	988	14
	other broadleaves	1186	12	217	23	171	21	216	29	154	31	1116	12	3060	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	31393	2	14963	4	10167	6	6389	6	8504	5	7494	5	78911	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10643	5	10582	5	6294	7	2966	9	6914	6	2301	12	39700	2
	fir	6565	6	3375	9	2174	11	344	27	308	23	292	23	13058	4
	pine	1589	11	226	52	159	30	320	21	633	17	111	47	3038	8
	larch	283	25	78	42	207	51	1938	11	1519	13	1163	13	5187	7
	Arolla pine	0	.	18	91	4	100	164	26	337	18	0	.	523	14
	other conifers	245	33	0	.	0	.	0	.	0	.	11	100	256	32
	beech	11516	4	2600	10	2594	10	116	35	243	39	954	12	18022	3
	maple	1114	10	332	18	450	16	45	53	31	61	31	79	2004	8
	ash	2428	9	315	24	616	18	47	52	16	76	146	29	3568	7
	oak	1407	12	68	46	40	52	27	47	61	57	191	31	1794	10
	sweet chestnut	9	100	4	100	0	.	9	73	3	100	595	15	620	14
	other broadleaves	798	15	117	32	134	30	24	125	42	47	504	16	1619	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	36598	2	17714	4	12671	5	5998	7	10108	5	6300	6	89388	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10186	6	10075	6	6669	8	2425	11	5940	8	1932	12	37227	3
	fir	6799	6	3984	9	2161	12	294	31	454	27	442	27	14134	4
	pine	1284	14	69	53	138	44	302	31	468	20	60	61	2321	10
	larch	443	22	131	43	133	41	2290	11	1865	15	1172	11	6034	7
	Arolla pine	0	.	0	0	0	.	119	31	184	24	7	100	310	19
	other conifers	197	44	0	.	0	.	0	.	0	.	16	100	214	41
	beech	9330	5	2344	13	1747	12	76	59	171	34	713	16	14382	4
	maple	561	17	184	33	179	30	0	.	47	55	21	71	992	13
	ash	1717	12	166	38	231	28	7	123	7	100	58	61	2186	11
	oak	1286	14	46	58	23	72	17	74	63	53	66	41	1501	13
	sweet chestnut	39	100	0	.	0	.	0	.	5	100	526	19	570	19
	other broadleaves	493	27	265	52	83	37	79	46	20	87	109	34	1048	19
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	32335	3	17265	5	11363	6	5609	7	9225	7	5122	6	80919	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5475	8	5296	8	4062	10	1297	16	3330	11	1172	16	20632	4
	fir	5089	8	3141	12	1166	17	104	59	234	32	129	38	9864	6
	pine	483	23	76	55	39	71	39	58	209	33	10	100	856	16
	larch	221	36	91	70	41	73	1417	14	828	16	700	17	3298	9
	Arolla pine	0	.	27	73	11	100	46	45	152	30	0	.	236	23
	other conifers	216	37	0	.	0	.	0	.	0	.	0	.	216	37
	beech	4121	8	1110	18	903	19	28	71	93	46	357	24	6612	7
	maple	135	39	56	54	52	79	0	.	0	.	0	.	243	30
	ash	683	22	102	45	106	45	0	.	0	.	44	71	936	18
	oak	787	19	16	100	22	100	17	100	0	.	28	65	870	18
	sweet chestnut	0	.	0	.	0	.	27	76	0	.	209	24	236	23
	other broadleaves	252	31	35	71	60	75	0	.	12	100	78	56	436	24
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	.	0	0
	total	17462	4	9951	6	6464	8	2973	10	4858	8	2727	9	44435	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2542	11	3014	11	2692	13	1080	24	1923	15	917	20	12168	6
	fir	2222	13	1411	16	1058	19	94	46	188	39	244	38	5217	8
	pine	40	72	0	.	0	.	0	.	55	59	0	.	95	46
	larch	24	100	55	73	97	52	867	19	415	22	409	24	1868	12
	Arolla pine	0	.	0	.	0	.	76	43	58	45	0	.	133	31
	other conifers	226	47	0	.	0	.	0	.	0	.	0	.	226	47
	beech	1992	13	347	31	362	32	12	100	70	71	172	36	2955	11
	maple	34	100	47	62	17	100	0	.	0	.	0	.	99	49
	ash	410	29	24	100	92	58	0	.	18	100	0	.	544	24
	oak	446	31	0	.	32	71	0	.	18	73	16	100	512	27
	sweet chestnut	0	.	0	.	0	.	10	100	0	.	250	25	260	24
	other broadleaves	132	46	21	100	0	.	22	100	0	.	52	82	228	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8069	7	4919	9	4348	11	2162	15	2745	11	2059	11	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	990	20	1202	17	617	33	351	32	813	20	531	23	4505	9
	fir	845	21	572	27	370	32	36	71	84	71	70	71	1976	14
	pine	27	100	0	.	0	.	0	.	29	100	0	.	56	71
	larch	30	100	0	.	0	.	342	27	202	41	272	35	847	19
	Arolla pine	0	.	0	.	0	.	52	50	69	45	0	.	120	34
	other conifers	73	71	41	100	0	.	0	.	0	.	0	.	114	58
	beech	642	25	106	60	159	52	21	100	0	.	69	51	997	20
	maple	43	100	12	100	14	100	0	.	0	.	0	.	69	68
	ash	92	65	0	.	38	100	0	.	0	.	0	.	130	54
	oak	287	32	23	100	68	72	0	.	0	.	0	.	378	28
	sweet chestnut	0	.	0	.	0	.	17	100	0	.	175	34	192	33
	other broadleaves	115	63	0	.	0	.	0	.	0	.	10	100	124	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3143	11	1957	14	1266	21	819	19	1197	16	1127	16	9509	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	112	58	356	37	406	34	27	100	127	58	174	43	1202	19
	fir	309	41	158	58	72	71	0	.	80	71	79	71	698	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	231	37	120	51	61	100	412	29
	Arolla pine	0	.	0	.	0	.	15	100	19	100	0	.	34	71
	other conifers	44	100	0	.	0	.	0	.	0	.	0	.	44	100
	beech	120	59	46	72	24	100	0	.	0	.	54	71	244	37
	maple	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	ash	34	100	0	.	0	.	0	.	0	.	0	.	34	100
	oak	165	65	0	.	0	.	0	.	0	.	0	.	165	65
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	67	55	67	55
	other broadleaves	120	74	0	.	0	.	46	100	0	.	0	.	167	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	905	24	560	29	502	30	320	32	345	33	446	28	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	118	58	126	52	214	44	62	71	547	45	262	42	1329	23
	fir	123	58	135	71	46	100	0	.	79	100	68	71	450	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	26	100	78	59	157	64	260	43
	Arolla pine	0	.	0	.	0	.	21	100	19	100	0	.	40	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	214	100	214	100
	maple	0	.	54	100	21	100	0	.	0	.	0	.	74	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	105	58	0	.	0	.	0	.	0	.	0	.	105	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	660	39	660	39
	other broadleaves	429	65	0	.	0	.	0	.	0	.	18	100	447	63
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	775	39	314	41	280	38	108	51	722	36	1378	27	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47948	3	45813	4	30065	6	14867	7	31510	5	11084	8	181288	2
	fir	31423	4	17632	6	9561	9	1523	20	1701	18	1829	18	63668	3
	pine	5129	8	683	32	678	25	2082	15	2817	13	401	42	11791	6
	larch	1550	15	452	34	666	29	9809	8	6738	9	6078	8	25291	4
	Arolla pine	0	.	83	68	42	71	1102	15	1619	14	15	60	2861	10
	other conifers	1475	22	92	52	35	57	12	50	63	31	29	93	1705	19
	beech	46217	3	11830	8	9928	7	926	21	1114	22	6315	9	76329	2
	maple	6408	6	2794	8	2436	10	344	19	297	35	442	18	12721	4
	ash	9869	6	1793	14	2453	12	405	23	236	25	1173	16	15928	5
	oak	6625	8	312	29	213	28	242	24	175	50	957	16	8524	7
	sweet chestnut	77	92	4	100	0	.	116	54	11	100	5352	10	5559	9
	other broadleaves	5734	9	1509	16	1127	13	1355	13	966	16	4698	7	15389	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	162454	1	82996	3	57202	4	32783	5	47246	4	38372	3	421054	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1218026. Birmensdorf, Swiss Federal Research Institute WSL

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