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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NFI1
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1983/85
		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	3624	9	2377	9	1397	14	1005	14	1480	11	330	17	10212	5
	fir	2011	10	831	12	400	18	62	40	40	37	61	43	3404	7
	pine	174	28	51	59	27	60	407	24	258	23	5	73	922	14
	larch	35	45	9	74	10	71	282	26	93	25	164	22	594	14
	Arolla pine	0	.	0	.	0	.	42	39	78	25	0	.	120	21
	other conifers	107	30	2	100	21	37	4	100	3	84	0	.	137	25
	beech	4037	6	1113	13	703	14	152	39	119	30	824	18	6947	5
	maple	699	11	352	16	252	21	50	41	16	55	39	38	1407	8
	ash	829	13	397	23	348	25	42	38	55	68	93	30	1765	10
	oak	528	22	29	37	7	78	40	39	1	100	144	37	750	17
	sweet chestnut	0	.	0	.	0	.	15	73	0	.	1069	15	1084	15
	other broadleaves	967	11	376	16	411	22	246	21	187	25	1096	12	3284	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13011	4	5537	6	3576	9	2348	10	2329	9	3825	7	30625	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7814	7	5720	8	4441	12	2265	13	3971	11	1085	19	25297	4
	fir	4780	9	1925	13	1039	17	131	46	77	42	68	46	8022	6
	pine	697	20	95	49	116	43	583	20	593	22	59	53	2143	11
	larch	186	37	19	77	20	82	671	19	422	20	417	26	1735	12
	Arolla pine	0	.	0	.	0	.	107	30	234	31	0	.	341	23
	other conifers	99	40	0	.	-4	100	0	.	0	.	0	.	95	42
	beech	8638	6	1950	12	2008	15	156	46	198	35	890	17	13840	5
	maple	1586	10	577	18	702	16	19	79	56	50	20	58	2960	8
	ash	1710	12	350	25	584	23	70	56	71	62	16	76	2803	9
	oak	1032	13	32	73	40	79	29	60	0	.	176	36	1309	12
	sweet chestnut	0	.	0	.	0	.	7	100	3	100	891	18	901	18
	other broadleaves	1310	14	196	28	286	25	96	32	76	49	804	19	2766	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	27852	3	10863	6	9233	8	4135	9	5701	9	4427	7	62212	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15350	6	10663	8	6102	10	2725	13	5815	10	1601	16	42257	3
	fir	7328	7	2814	12	1948	15	192	42	172	33	223	34	12677	5
	pine	2505	13	96	42	88	53	393	22	799	22	131	71	4013	10
	larch	325	29	74	54	0	.	1110	15	1197	16	687	23	3392	9
	Arolla pine	0	.	8	100	0	.	137	36	354	27	0	.	499	21
	other conifers	137	51	0	.	0	.	10	100	0	.	0	.	148	48
	beech	10575	6	2643	14	2168	15	58	65	270	39	534	18	16248	5
	maple	1043	14	385	28	509	20	24	100	15	71	12	89	1989	10
	ash	1911	12	308	26	270	28	24	100	17	100	-10	74	2521	10
	oak	1140	14	10	100	18	76	0	.	34	71	246	39	1448	13
	sweet chestnut	0	.	0	.	0	.	24	72	4	100	377	22	404	21
	other broadleaves	1085	15	206	36	181	31	61	46	98	61	353	29	1983	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	41400	3	17207	6	11284	7	4758	9	8776	8	4153	9	87578	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16575	6	11101	7	6890	9	2559	13	5799	10	1793	16	44717	3
	fir	6297	8	3517	10	1994	16	266	33	293	42	400	31	12767	5
	pine	2563	13	349	49	121	64	305	38	509	24	15	100	3863	11
	larch	346	30	69	45	86	56	1312	15	1169	17	729	17	3711	9
	Arolla pine	0	.	0	.	0	.	78	49	235	34	0	.	313	29
	other conifers	266	57	0	.	0	.	0	.	0	.	0	.	266	57
	beech	8390	7	2164	16	1615	16	67	59	332	38	346	22	12914	6
	maple	532	19	155	28	180	31	0	.	29	59	0	.	897	14
	ash	1040	15	82	44	238	31	0	.	0	.	40	75	1401	13
	oak	1039	17	0	.	11	100	0	.	32	72	59	51	1141	16
	sweet chestnut	0	.	0	.	0	.	8	100	7	100	327	23	342	22
	other broadleaves	346	27	121	45	104	42	46	63	8	100	48	47	674	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	37396	3	17559	6	11238	7	4640	10	8413	8	3758	9	83004	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	10179	7	7288	9	5136	12	1825	15	4145	12	1440	18	30013	4
	fir	3342	11	2547	15	1180	19	175	44	209	43	96	50	7549	8
	pine	928	19	94	74	0	.	81	46	392	34	32	71	1527	16
	larch	215	34	225	42	190	53	1033	17	1080	20	637	23	3379	10
	Arolla pine	0	.	0	.	0	.	21	71	172	39	0	.	192	36
	other conifers	87	76	0	.	0	.	0	.	0	.	0	.	87	76
	beech	4154	10	1548	20	705	27	40	100	186	54	363	36	6997	8
	maple	264	40	38	59	53	71	0	.	13	100	0	.	368	31
	ash	452	24	65	58	30	100	0	.	25	100	0	.	571	21
	oak	670	21	0	.	0	.	0	.	0	.	21	100	691	20
	sweet chestnut	0	.	0	.	0	.	11	100	0	.	168	28	179	27
	other broadleaves	226	33	115	52	23	100	50	84	0	.	20	67	434	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	20517	5	11920	7	7317	10	3236	11	6221	9	2777	12	51988	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3531	11	2693	13	2542	16	649	30	1572	18	881	28	11868	7
	fir	1236	16	1260	22	192	41	60	71	75	100	94	59	2917	12
	pine	251	39	40	100	31	100	0	.	90	51	0	.	412	29
	larch	59	71	35	100	109	58	607	25	568	25	290	33	1668	15
	Arolla pine	0	.	0	.	0	.	12	100	72	65	0	.	84	58
	other conifers	38	100	42	100	0	.	0	.	0	.	0	.	81	71
	beech	1863	17	423	33	132	54	9	100	162	51	104	40	2693	14
	maple	146	50	0	.	0	.	0	.	0	.	0	.	146	50
	ash	173	48	0	.	42	100	0	.	0	.	0	.	215	43
	oak	147	45	0	.	50	100	0	.	0	.	0	.	197	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	107	44	107	44
	other broadleaves	29	100	31	100	33	100	29	100	0	.	15	100	138	46
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7473	8	4525	11	3131	15	1366	19	2538	14	1492	19	20526	5
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1337	20	842	23	1321	25	335	35	855	22	461	41	5151	11
	fir	953	24	826	27	215	54	0	.	124	70	190	61	2308	16
	pine	36	100	0	.	0	.	0	.	38	100	0	.	73	71
	larch	72	71	0	.	0	.	500	30	218	39	172	48	963	21
	Arolla pine	0	.	0	.	0	.	78	52	36	72	0	.	114	42
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	387	36	124	63	33	100	35	100	0	.	37	100	617	27
	maple	0	.	14	100	32	71	0	.	0	.	0	.	47	58
	ash	113	62	0	.	0	.	0	.	0	.	0	.	113	62
	oak	266	38	0	.	18	100	0	.	0	.	0	.	285	36
	sweet chestnut	0	.	0	.	0	.	19	100	0	.	254	47	273	45
	other broadleaves	32	100	0	.	0	.	0	.	0	.	0	.	32	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3197	13	1806	17	1620	22	968	21	1271	18	1114	24	9975	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	398	36	141	52	379	35	76	71	198	46	62	75	1253	19
	fir	49	100	432	39	58	100	50	100	0	.	123	73	712	30
	pine	71	100	0	.	0	.	0	.	0	.	0	.	71	100
	larch	0	.	0	.	0	.	74	73	99	58	0	.	173	46
	Arolla pine	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	350	42	0	.	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	267	46	0	.	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	1135	25	572	32	471	32	224	42	344	33	302	41	3048	14
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	241	51	77	100	201	48	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	.	95	72	0	.	58	100	154	59
	Arolla pine	0	.	0	.	0	.	34	100	0	.	0	.	34	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	102	75	0	.	0	.	0	.	0	.	0	.	102	75
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	38	100	0	.	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	509	34	77	100	201	48	129	59	147	58	175	48	1239	21
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	102	73	0	.	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	.	50	100	64	100	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	102	73	0	.	30	100	82	72	611	42	893	32
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59049	4	41002	6	28409	8	11439	10	23983	7	7734	12	171616	2
	fir	26193	6	14152	8	7026	11	936	28	989	27	1358	27	50654	4
	pine	7223	10	727	43	382	35	1769	18	2679	17	243	51	13024	7
	larch	1239	20	431	33	415	37	5684	11	4895	13	3218	14	15882	6
	Arolla pine	0	.	8	100	0	.	563	23	1212	21	0	.	1783	16
	other conifers	734	35	44	96	17	43	15	77	3	84	0	.	813	32
	beech	38496	4	9965	10	7400	11	518	31	1314	31	3098	13	60791	3
	maple	4271	8	1522	14	1728	13	93	41	128	38	71	35	7814	6
	ash	6229	8	1202	17	1513	18	136	40	168	53	140	30	9388	7
	oak	5128	10	71	42	144	48	69	42	68	71	645	24	6125	9
	sweet chestnut	0	.	0	.	0	.	83	51	14	100	3791	14	3888	13
	other broadleaves	3995	10	1045	18	1038	17	528	27	370	30	2335	12	9311	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	152557	2	70168	4	48073	6	21833	7	35822	6	22635	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. 1217849. Birmensdorf, Swiss Federal Research Institute WSL

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