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: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	1233	12	1868	9	3129	6	865	11	1960	8	693	14	9748	4
	fir	886	9	1151	11	1048	9	84	37	34	32	101	32	3303	6
	pine	15	89	38	60	81	41	269	23	311	16	9	55	723	13
	larch	9	91	47	42	44	33	363	14	255	20	300	14	1018	9
	Arolla pine	0	.	0	.	5	67	82	31	138	25	4	63	229	19
	other conifers	29	62	40	35	39	59	5	67	41	31	4	60	157	22
	beech	1317	8	1291	9	1332	9	105	44	117	23	1029	11	5192	4
	maple	450	12	605	13	776	9	77	25	54	39	157	19	2120	6
	ash	263	19	535	16	435	13	99	45	83	30	242	19	1657	8
	oak	95	30	142	22	31	41	71	26	5	65	126	25	468	12
	sweet chestnut	1	100	4	72	15	76	0	0	0	.	667	16	687	15
	other broadleaves	346	11	671	11	843	10	418	15	480	16	1368	9	4127	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4644	5	6393	5	7779	4	2438	7	3477	6	4699	5	29431	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2405	9	4253	8	7611	5	2227	11	4074	7	1227	11	21798	3
	fir	1633	10	1602	11	2067	9	188	31	131	25	165	27	5787	5
	pine	210	28	182	30	192	26	564	19	453	17	61	43	1662	10
	larch	18	100	142	27	61	44	788	12	531	15	830	13	2370	7
	Arolla pine	0	.	0	.	17	72	273	19	271	20	4	71	566	13
	other conifers	85	39	111	29	50	42	0	0	23	40	2	100	271	19
	beech	3396	8	2831	6	3208	8	160	45	283	25	1547	12	11425	4
	maple	782	11	1010	13	1702	10	60	39	149	37	127	28	3830	6
	ash	481	16	1095	12	974	13	99	31	79	33	405	25	3132	7
	oak	329	23	544	18	66	38	62	31	5	71	245	22	1250	11
	sweet chestnut	0	.	7	100	7	72	8	100	0	.	1256	12	1278	12
	other broadleaves	405	15	825	12	762	13	372	17	242	25	1527	10	4133	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	9745	4	12602	4	16717	4	4801	7	6240	6	7396	4	57502	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3444	8	5988	7	12630	5	2962	9	5858	7	2098	12	32979	3
	fir	2813	9	2487	9	3329	7	139	33	161	30	250	31	9180	5
	pine	646	18	616	16	337	26	562	17	704	19	176	56	3041	8
	larch	42	44	313	25	74	34	1393	12	980	13	1194	12	3997	7
	Arolla pine	0	.	0	.	43	62	255	19	353	22	20	79	671	14
	other conifers	59	70	157	37	-9	144	0	.	0	.	0	.	208	35
	beech	6048	6	4305	7	4221	7	117	31	383	22	1211	12	16286	4
	maple	889	12	873	15	1297	10	17	73	109	42	94	36	3279	7
	ash	662	15	1686	11	1017	15	45	52	52	41	278	24	3741	7
	oak	425	22	640	15	84	49	23	48	20	86	290	20	1481	10
	sweet chestnut	0	.	16	82	13	100	10	71	0	.	949	14	988	14
	other broadleaves	370	17	817	16	438	17	155	29	137	32	1144	12	3060	7
	indeterminable	0	0	0	0	0	0	0	.	0	0	0	0	0	0
	total	15398	4	17899	4	23474	3	5679	7	8756	5	7705	5	78911	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5181	7	6525	7	16034	4	2628	10	6957	6	2374	12	39700	2
	fir	3827	8	3477	8	4862	7	226	30	375	20	292	23	13058	4
	pine	697	16	937	14	336	36	312	21	643	16	111	47	3038	8
	larch	25	67	257	27	162	40	1793	12	1652	13	1297	12	5187	7
	Arolla pine	0	.	0	.	22	77	164	26	337	18	0	.	523	14
	other conifers	39	89	206	35	0	.	0	.	0	.	11	100	256	32
	beech	6255	6	6007	6	4438	7	71	56	294	34	957	12	18022	3
	maple	625	13	556	15	725	12	28	79	40	53	31	79	2004	8
	ash	597	18	1938	10	843	15	19	72	25	57	146	29	3568	7
	oak	386	21	1040	14	89	38	14	62	50	56	216	30	1794	10
	sweet chestnut	0	.	9	100	13	59	0	.	0	.	598	15	620	14
	other broadleaves	190	24	620	18	266	21	-3	753	42	47	504	16	1619	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	17821	3	21573	3	27788	3	5251	7	10416	5	6539	5	89388	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4764	8	6341	8	16169	5	1954	11	5953	8	2047	11	37227	3
	fir	3496	9	4278	9	5155	7	222	37	541	24	442	27	14134	4
	pine	420	28	874	16	162	33	302	31	504	20	60	61	2321	10
	larch	52	50	407	24	193	35	2180	11	1880	15	1323	10	6034	7
	Arolla pine	0	.	0	.	0	0	119	31	184	24	7	100	310	19
	other conifers	0	.	197	44	0	.	0	.	0	.	16	100	214	41
	beech	4130	8	5602	7	3705	9	20	100	212	32	713	16	14382	4
	maple	287	21	329	25	292	25	0	.	64	44	21	71	992	13
	ash	327	22	1486	14	289	26	7	123	19	73	58	61	2186	11
	oak	224	32	1064	16	78	42	6	100	49	62	79	38	1501	13
	sweet chestnut	0	.	39	100	0	.	0	.	0	.	531	19	570	19
	other broadleaves	69	51	449	28	336	42	65	52	10	140	119	33	1048	19
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	13769	4	21065	4	26379	3	4874	8	9416	7	5416	6	80919	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2625	10	3540	10	8907	6	905	18	3381	11	1274	15	20632	4
	fir	2380	12	3184	10	3812	10	104	59	255	30	129	38	9864	6
	pine	133	41	403	25	60	64	39	58	209	33	10	100	856	16
	larch	0	.	221	36	203	54	1216	15	792	16	865	15	3298	9
	Arolla pine	0	.	0	.	38	59	46	45	152	30	0	.	236	23
	other conifers	27	100	189	40	0	.	0	.	0	.	0	.	216	37
	beech	1227	15	3261	10	1650	14	13	100	105	42	357	24	6612	7
	maple	20	100	115	42	108	47	0	.	0	.	0	.	243	30
	ash	119	39	582	25	191	34	0	.	0	.	44	71	936	18
	oak	165	57	644	19	33	71	0	.	0	.	28	65	870	18
	sweet chestnut	0	.	0	.	27	76	0	.	0	.	209	24	236	23
	other broadleaves	68	52	224	36	55	58	0	.	12	100	78	56	436	24
	indeterminable	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	6763	7	12365	5	15084	5	2323	11	4906	8	2994	9	44435	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1116	18	1725	14	5730	9	675	28	1933	15	989	19	12168	6
	fir	1081	18	1521	16	2030	13	94	46	246	38	244	38	5217	8
	pine	16	100	24	100	0	.	0	.	55	59	0	.	95	46
	larch	24	100	43	72	110	52	682	21	375	24	634	21	1868	12
	Arolla pine	0	.	0	.	0	.	76	43	58	45	0	.	133	31
	other conifers	36	100	190	52	0	.	0	.	0	.	0	.	226	47
	beech	561	25	1593	15	531	25	0	.	97	58	172	36	2955	11
	maple	34	100	0	.	47	62	0	.	17	100	0	.	99	49
	ash	84	50	385	31	57	71	0	.	18	100	0	.	544	24
	oak	39	71	424	32	16	100	0	.	18	73	16	100	512	27
	sweet chestnut	0	.	0	.	10	100	0	.	0	.	250	25	260	24
	other broadleaves	0	.	101	52	74	59	0	.	0	.	52	82	228	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2991	11	6007	8	8605	7	1527	17	2816	11	2356	11	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	324	38	730	22	1809	16	272	38	814	20	557	22	4505	9
	fir	349	32	624	25	834	22	16	100	84	71	70	71	1976	14
	pine	0	.	27	100	0	.	0	.	29	100	0	.	56	71
	larch	0	.	30	100	0	.	315	28	175	44	326	32	847	19
	Arolla pine	0	.	0	.	0	.	52	50	58	51	11	100	120	34
	other conifers	39	100	75	71	0	.	0	.	0	.	0	.	114	58
	beech	74	58	702	25	131	52	21	100	0	.	69	51	997	20
	maple	0	.	43	100	26	71	0	.	0	.	0	.	69	68
	ash	0	.	130	54	0	.	0	.	0	.	0	.	130	54
	oak	84	61	226	36	68	72	0	.	0	.	0	.	378	28
	sweet chestnut	0	.	0	.	0	.	17	100	0	.	175	34	192	33
	other broadleaves	0	.	115	63	0	.	0	.	0	.	10	100	124	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	870	21	2701	12	2869	12	692	20	1161	17	1217	15	9509	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	122	58	692	27	27	100	150	51	174	43	1202	19
	fir	245	48	114	59	181	51	0	.	80	71	79	71	698	26
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	196	39	86	59	129	60	412	29
	Arolla pine	0	.	0	.	0	.	15	100	19	100	0	.	34	71
	other conifers	0	.	44	100	0	.	0	.	0	.	0	.	44	100
	beech	37	100	83	72	70	58	0	.	0	.	54	71	244	37
	maple	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	ash	0	.	34	100	0	.	0	.	0	.	0	.	34	100
	oak	45	100	120	82	0	.	0	.	0	.	0	.	165	65
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	67	55	67	55
	other broadleaves	0	.	120	74	46	100	0	.	0	.	0	.	167	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	362	38	637	29	989	22	239	35	335	33	514	26	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39	100	119	58	333	34	29	100	525	47	283	40	1329	23
	fir	0	.	88	71	215	52	0	.	79	100	68	71	450	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	26	100	50	72	185	56	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	.	0	.	74	77	0	.	0	.	0	.	74	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	105	58	0	.	0	.	0	.	0	.	105	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	660	39	660	39
	other broadleaves	187	75	242	100	0	.	0	.	0	.	18	100	447	63
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	226	64	555	48	623	27	75	58	673	39	1428	26	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21167	5	31211	5	73044	3	12544	7	31605	5	11716	8	181288	2
	fir	16709	6	18527	6	23533	5	1073	25	1986	17	1838	18	63668	3
	pine	2138	13	3102	10	1169	22	2048	15	2908	13	427	40	11791	6
	larch	171	31	1461	15	847	28	8951	8	6776	9	7085	8	25291	4
	Arolla pine	0	.	0	.	125	51	1102	15	1589	14	45	69	2861	10
	other conifers	313	43	1210	24	80	38	5	67	64	30	33	83	1705	19
	beech	23045	4	25676	4	19286	5	508	30	1491	18	6323	9	76329	2
	maple	3087	7	3530	8	5047	6	182	26	433	30	442	18	12721	4
	ash	2532	11	7872	7	3807	9	269	28	275	25	1173	16	15928	5
	oak	1792	15	4948	9	463	22	175	28	146	53	1000	15	8524	7
	sweet chestnut	1	100	76	93	85	69	36	63	0	.	5363	10	5559	9
	other broadleaves	1635	13	4186	12	2820	10	1007	14	923	15	4818	7	15389	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	72589	2	101798	2	130306	2	27900	5	48196	4	40265	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. 1218032. Birmensdorf, Swiss Federal Research Institute WSL

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