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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI1
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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	1350	16	2524	11	3516	8	953	14	1537	11	334	17	10212	5
	fir	1045	12	1162	14	1033	11	50	48	53	32	61	43	3404	7
	pine	100	38	92	39	60	49	400	25	258	23	12	65	922	14
	larch	2	100	33	47	9	74	275	26	101	24	174	21	594	14
	Arolla pine	0	.	0	.	0	.	42	39	75	26	3	100	120	21
	other conifers	22	38	82	36	26	50	0	.	3	84	4	100	137	25
	beech	2113	9	2148	8	1617	10	81	55	164	27	824	18	6947	5
	maple	389	15	314	17	595	12	20	45	51	59	39	38	1407	8
	ash	270	21	669	15	620	18	50	39	62	61	93	30	1765	10
	oak	94	30	449	26	26	36	35	43	0	.	145	37	750	17
	sweet chestnut	0	.	0	.	0	.	15	73	0	.	1069	15	1084	15
	other broadleaves	307	17	685	13	762	14	220	22	200	24	1110	12	3284	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5691	6	8159	5	8263	5	2141	11	2502	9	3868	7	30625	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2823	11	5665	9	9698	7	2013	14	3984	11	1114	18	25297	4
	fir	2418	12	3085	12	2265	11	81	54	104	35	68	46	8022	6
	pine	334	30	388	27	168	35	568	21	611	22	75	47	2143	11
	larch	48	51	138	46	19	77	659	20	437	19	433	26	1735	12
	Arolla pine	0	.	0	.	0	.	107	30	234	31	0	.	341	23
	other conifers	0	.	95	42	0	3103	0	.	0	.	0	.	95	42
	beech	4800	8	4376	9	3350	9	125	55	300	32	890	17	13840	5
	maple	878	13	706	16	1223	12	3	347	130	38	20	58	2960	8
	ash	668	20	1284	14	728	18	17	95	91	51	16	76	2803	9
	oak	259	24	765	16	57	48	22	72	30	100	176	36	1309	12
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	894	18	901	18
	other broadleaves	354	21	991	17	447	19	84	36	87	44	804	19	2766	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12581	5	17494	5	17953	5	3686	10	6008	9	4490	7	62212	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4556	9	12429	7	15321	6	2457	14	5893	10	1601	16	42257	3
	fir	3426	10	4665	10	4023	10	124	55	216	29	223	34	12677	5
	pine	867	20	1647	17	137	35	393	22	837	21	131	71	4013	10
	larch	75	50	246	34	78	51	1086	16	1159	17	748	21	3392	9
	Arolla pine	0	.	0	.	8	100	137	36	354	27	0	.	499	21
	other conifers	0	.	137	51	0	.	0	.	0	.	10	100	148	48
	beech	5546	8	5749	9	4017	11	57	66	345	33	534	18	16248	5
	maple	700	17	523	22	660	17	24	100	69	54	12	89	1989	10
	ash	490	23	1539	13	461	22	24	100	17	100	-10	74	2521	10
	oak	245	30	905	16	12	100	0	.	21	78	265	37	1448	13
	sweet chestnut	0	.	0	.	0	.	24	72	0	.	380	22	404	21
	other broadleaves	385	25	715	18	353	25	49	52	129	52	353	29	1983	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16290	5	28555	4	25070	4	4376	10	9040	8	4247	9	87578	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5814	8	12598	7	16653	6	1978	14	5830	10	1845	15	44717	3
	fir	2739	11	4264	10	4841	9	165	43	359	35	400	31	12767	5
	pine	568	20	2020	15	441	43	310	37	509	24	15	100	3863	11
	larch	47	74	298	33	121	43	1269	16	1192	17	783	17	3711	9
	Arolla pine	0	.	0	.	0	.	78	49	235	34	0	.	313	29
	other conifers	123	100	143	63	0	.	0	.	0	.	0	.	266	57
	beech	4043	9	4571	9	3429	12	42	73	484	31	346	22	12914	6
	maple	286	23	288	29	264	23	0	.	59	41	0	.	897	14
	ash	231	29	858	17	261	27	0	.	11	100	40	75	1401	13
	oak	119	41	920	18	11	100	0	.	18	100	72	46	1141	16
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	334	23	342	22
	other broadleaves	93	52	253	32	239	29	32	80	8	100	48	47	674	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14064	5	26213	4	26266	4	3873	11	8705	8	3884	9	83004	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3793	11	7345	9	11843	7	1385	16	4165	12	1482	18	30013	4
	fir	1510	17	2344	15	3135	12	151	49	313	40	96	50	7549	8
	pine	240	33	687	24	94	74	81	46	392	34	32	71	1527	16
	larch	21	100	215	34	290	40	970	17	1184	19	699	22	3379	10
	Arolla pine	0	.	0	.	0	.	21	71	172	39	0	.	192	36
	other conifers	26	100	61	100	0	.	0	.	0	.	0	.	87	76
	beech	1586	17	2866	13	1935	17	40	100	207	50	363	36	6997	8
	maple	90	75	174	46	92	48	0	.	13	100	0	.	368	31
	ash	40	71	426	26	81	50	0	.	25	100	0	.	571	21
	oak	86	55	584	22	0	.	0	.	0	.	21	100	691	20
	sweet chestnut	0	.	0	.	11	100	0	.	0	.	168	28	179	27
	other broadleaves	40	71	186	38	179	42	9	100	0	.	20	67	434	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7432	8	14888	6	17659	6	2656	12	6471	9	2882	12	51988	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1119	20	2965	13	4737	11	559	34	1563	18	925	27	11868	7
	fir	650	22	844	24	1254	20	0	.	75	100	94	59	2917	12
	pine	71	72	210	42	40	100	0	.	90	51	0	.	412	29
	larch	0	.	131	50	72	71	576	26	568	25	320	31	1668	15
	Arolla pine	0	.	0	.	0	.	12	100	72	65	0	.	84	58
	other conifers	0	.	81	71	0	.	0	.	0	.	0	.	81	71
	beech	538	31	1529	19	352	33	9	100	162	51	104	40	2693	14
	maple	34	100	112	58	0	.	0	.	0	.	0	.	146	50
	ash	23	100	192	47	0	.	0	.	0	.	0	.	215	43
	oak	0	.	197	42	0	.	0	.	0	.	0	.	197	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	107	44	107	44
	other broadleaves	0	.	29	100	94	58	0	.	0	.	15	100	138	46
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2436	14	6289	9	6549	9	1156	22	2530	14	1566	18	20526	5
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	551	32	877	24	2128	18	278	37	817	22	500	39	5151	11
	fir	674	29	417	34	904	26	0	.	124	70	190	61	2308	16
	pine	0	.	36	100	0	.	0	.	38	100	0	.	73	71
	larch	34	100	39	100	0	.	400	35	218	39	272	37	963	21
	Arolla pine	0	.	0	.	0	.	78	52	21	100	15	100	114	42
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	453	34	92	58	35	100	0	.	37	100	617	27
	maple	0	.	0	.	31	71	0	.	16	100	0	.	47	58
	ash	0	.	113	62	0	.	0	.	0	.	0	.	113	62
	oak	0	.	266	38	18	100	0	.	0	.	0	.	285	36
	sweet chestnut	0	.	0	.	0	.	19	100	0	.	254	47	273	45
	other broadleaves	0	.	32	100	0	.	0	.	0	.	0	.	32	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1259	22	2233	14	3172	14	811	23	1233	18	1267	22	9975	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	136	59	322	41	459	30	76	71	198	46	62	75	1253	19
	fir	49	100	0	.	540	34	0	.	0	.	123	73	712	30
	pine	0	.	71	100	0	.	0	.	0	.	0	.	71	100
	larch	0	.	0	.	0	.	28	100	99	58	47	100	173	46
	Arolla pine	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	350	42	34	100	0	.	47	78	0	.	431	36
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	267	46	0	.	0	.	0	.	0	.	267	46
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	118	60	118	60
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	186	51	1010	27	1033	23	127	51	344	33	349	38	3048	14
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	99	71	142	71	278	44	0	.	147	58	0	.	666	29
	fir	128	71	0	.	0	.	0	.	0	.	0	.	128	71
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	37	100	0	.	116	71	154	59
	Arolla pine	0	.	0	.	0	.	34	100	0	.	0	.	34	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	102	75	0	.	0	.	0	.	0	.	102	75
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	38	100	0	.	0	.	0	.	0	.	38	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	117	53	117	53
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	227	51	282	47	278	44	71	71	147	58	233	44	1239	21
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	102	73	0	0	0	.	80	100	182	60
	fir	68	100	0	.	0	.	0	.	0	.	104	100	172	72
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	114	71	114	71
	Arolla pine	0	.	0	.	0	.	30	100	32	100	0	.	62	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	364	58	364	58
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68	100	0	.	102	73	30	100	32	100	662	40	893	32
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20242	6	44868	5	64733	4	9696	10	24135	7	7942	12	171616	2
	fir	12706	8	16782	8	17994	7	571	38	1243	25	1358	27	50654	4
	pine	2181	15	5151	12	940	35	1752	18	2735	17	265	48	13024	7
	larch	227	36	1100	22	590	29	5302	11	4958	13	3706	14	15882	6
	Arolla pine	0	.	0	.	8	100	563	23	1195	21	18	100	1783	16
	other conifers	171	87	599	36	25	64	0	.	3	84	15	77	813	32
	beech	18626	6	22143	6	14825	8	391	38	1708	26	3098	13	60791	3
	maple	2377	11	2117	12	2865	9	46	58	338	37	71	35	7814	6
	ash	1722	16	5081	9	2150	13	90	47	205	45	140	30	9388	7
	oak	804	19	4391	11	125	33	57	49	69	71	679	24	6125	9
	sweet chestnut	0	.	0	.	18	100	64	59	0	.	3805	13	3888	13
	other broadleaves	1179	16	2891	12	2073	13	394	22	424	28	2349	12	9311	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	60233	3	105123	3	106345	3	18928	8	37012	6	23447	5	351089	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. 1217858. Birmensdorf, Swiss Federal Research Institute WSL

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