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
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: protection forest region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		protection forest region
		Jura + Plateau		Northwestern Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	20545	6	18648	6	11063	8	8897	8	17316	7	5458	10	81927	3
	fir	12416	6	6393	8	3125	12	1062	24	319	30	696	27	24011	4
	pine	680	26	807	34	343	39	2917	18	3546	17	106	55	8400	10
	larch	437	44	397	37	225	38	4332	11	2471	17	3426	12	11289	7
	Arolla pine	0	.	11	100	65	76	787	20	1479	17	38	61	2380	12
	other conifers	1135	21	184	51	401	30	122	47	929	29	40	79	2810	14
	beech	18249	4	6086	9	4931	10	1275	23	892	20	7165	10	38597	3
	maple	6740	7	3227	11	2187	15	1020	18	453	27	1302	19	14930	5
	ash	4963	8	1500	15	1461	17	612	21	591	26	1837	15	10964	6
	oak	2017	18	274	48	54	45	933	26	42	50	1213	25	4532	12
	sweet chestnut	50	52	0	.	0	.	172	71	0	.	7802	12	8024	11
	other broadleaves	8503	7	5326	10	3672	14	5186	11	4700	14	12797	7	40183	4
	indeterminable	238	24	282	21	61	41	89	33	122	34	164	31	955	12
	total	75973	2	43133	4	27586	5	27404	5	32860	5	42045	4	249002	1
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12110	6	12091	6	7125	8	6301	8	10264	6	3254	10	51145	3
	fir	6006	6	3306	9	1616	13	615	21	249	25	432	24	12224	4
	pine	1032	16	362	36	349	31	2038	15	1465	19	105	43	5351	9
	larch	295	27	61	57	126	43	2528	11	1643	13	2361	11	7013	6
	Arolla pine	0	.	32	100	20	100	871	17	1021	20	10	100	1954	13
	other conifers	462	22	94	51	31	58	21	71	122	39	0	.	730	17
	beech	11653	5	4159	9	2963	10	682	23	550	21	3851	11	23858	4
	maple	3830	8	1879	11	1730	13	314	29	220	38	280	25	8252	5
	ash	2601	9	911	17	1194	17	399	25	241	39	1020	21	6366	7
	oak	1846	14	202	35	31	75	262	26	39	62	661	19	3041	10
	sweet chestnut	29	100	0	.	0	.	50	53	9	100	4041	11	4130	10
	other broadleaves	2930	9	1107	16	1182	17	1443	14	972	20	4755	9	12389	5
	indeterminable	59	41	69	38	77	39	82	35	71	38	20	71	377	17
	total	42853	3	24272	4	16445	5	15605	5	16866	5	20790	4	136830	1
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7419	5	8312	5	4962	7	3524	8	6260	6	2109	11	32586	2
	fir	4101	6	2027	8	1098	12	206	26	145	31	261	29	7837	4
	pine	1359	12	222	35	267	35	827	15	823	18	184	51	3683	8
	larch	268	22	61	49	109	35	1827	11	1131	12	1436	11	4831	6
	Arolla pine	0	.	46	88	26	86	409	20	517	19	10	100	1009	13
	other conifers	205	31	4	100	20	71	0	.	0	.	0	.	229	28
	beech	8442	4	2445	10	2020	9	198	25	310	24	1387	12	14801	3
	maple	1668	8	708	13	663	15	70	35	95	40	163	32	3368	6
	ash	1908	9	526	22	475	17	219	55	52	47	293	24	3474	8
	oak	984	13	85	41	35	47	40	46	22	84	367	19	1532	10
	sweet chestnut	25	86	0	.	0	.	38	66	0	.	1446	13	1509	12
	other broadleaves	1181	11	285	22	255	20	284	26	222	28	1577	10	3803	6
	indeterminable	45	52	52	41	28	74	14	76	37	61	19	66	195	24
	total	27605	2	14773	4	9957	5	7658	6	9614	5	9251	4	78857	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5134	4	5875	5	3275	7	1938	8	3858	6	1359	10	21440	2
	fir	3018	6	1559	8	922	10	186	26	142	22	171	22	5998	4
	pine	870	10	161	36	120	28	262	20	411	15	73	42	1897	7
	larch	138	24	44	37	79	42	1304	11	921	12	813	12	3299	6
	Arolla pine	0	.	24	63	4	100	155	24	265	16	0	.	448	13
	other conifers	109	31	0	.	0	.	0	.	0	.	4	100	113	30
	beech	5497	4	1306	9	1382	10	75	32	156	28	711	12	9126	3
	maple	622	9	193	16	285	14	36	54	21	60	29	65	1186	7
	ash	1110	8	162	20	318	17	31	46	9	72	94	27	1726	7
	oak	701	11	44	41	18	50	28	43	36	58	131	28	958	10
	sweet chestnut	8	100	4	100	0	.	8	71	4	100	565	14	588	13
	other broadleaves	487	14	82	28	87	27	45	33	31	39	372	13	1103	8
	indeterminable	16	50	37	37	16	61	8	71	26	41	4	100	106	21
	total	17710	2	9491	4	6505	5	4076	6	5878	5	4326	5	47987	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3124	5	3433	6	2203	7	995	10	2156	7	791	11	12703	3
	fir	2048	6	1162	8	595	12	116	27	120	23	136	26	4178	4
	pine	411	13	33	52	55	44	134	29	191	19	23	52	847	10
	larch	130	21	51	40	36	40	937	10	710	14	547	11	2410	6
	Arolla pine	0	.	8	100	0	.	68	29	111	21	4	100	191	17
	other conifers	56	42	0	.	0	.	0	.	0	.	4	100	60	40
	beech	2825	5	726	13	605	11	28	58	58	31	333	14	4574	4
	maple	204	16	79	37	88	25	0	.	20	52	9	71	399	13
	ash	489	12	53	35	79	26	8	71	4	100	16	60	650	10
	oak	435	14	17	60	8	71	8	71	24	53	31	42	523	12
	sweet chestnut	15	100	0	.	0	.	0	.	4	100	306	17	325	17
	other broadleaves	169	25	88	53	38	33	37	45	18	51	58	30	407	17
	indeterminable	5	100	8	71	4	100	12	58	8	71	0	.	37	33
	total	9911	3	5659	4	3711	6	2343	7	3423	6	2258	6	27305	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1170	7	1289	7	986	9	370	14	834	10	342	14	4991	4
	fir	1054	8	649	11	234	17	27	55	50	29	36	33	2050	6
	pine	109	23	19	51	8	71	12	58	62	31	4	100	214	16
	larch	53	34	16	62	10	73	418	14	233	15	201	16	931	8
	Arolla pine	0	.	10	72	8	71	19	45	59	30	0	.	97	23
	other conifers	40	37	0	.	0	.	0	.	0	.	0	.	40	37
	beech	861	8	231	17	214	17	12	58	20	45	117	22	1455	6
	maple	28	38	20	52	14	77	0	.	0	.	0	.	63	30
	ash	146	21	21	45	20	45	0	.	0	.	10	71	197	17
	oak	171	18	5	100	5	100	5	100	0	.	13	58	198	17
	sweet chestnut	0	.	0	.	0	.	20	82	0	.	123	22	142	22
	other broadleaves	54	31	7	71	18	65	0	.	6	100	29	48	114	23
	indeterminable	4	100	4	100	0	.	4	100	0	.	0	.	12	58
	total	3690	4	2271	6	1518	8	887	10	1265	8	874	8	10504	2
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	439	11	609	10	478	13	218	23	382	14	202	17	2329	5
	fir	348	13	201	16	171	18	24	41	29	38	41	37	812	8
	pine	8	71	0	.	0	.	0	.	13	58	0	.	21	45
	larch	4	100	8	71	17	50	204	18	90	22	89	23	413	12
	Arolla pine	0	.	0	.	0	.	27	43	19	45	0	.	46	31
	other conifers	33	47	0	.	0	.	0	.	0	.	0	.	33	47
	beech	308	13	68	28	59	30	4	100	11	74	49	37	499	10
	maple	5	100	12	58	5	100	0	.	0	.	0	.	21	45
	ash	71	29	4	100	14	59	0	.	4	100	0	.	93	24
	oak	71	31	0	.	9	71	0	.	8	71	4	100	93	26
	sweet chestnut	0	.	0	.	0	.	5	100	0	.	110	23	115	22
	other broadleaves	23	46	4	100	0	.	4	100	0	.	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1308	6	905	8	754	10	486	14	556	11	506	10	4515	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	125	20	189	17	95	33	66	32	122	19	103	21	700	9
	fir	108	20	72	26	45	32	8	71	8	71	13	57	253	13
	pine	4	100	0	.	0	.	0	.	4	100	0	.	8	71
	larch	4	100	0	.	0	.	65	26	38	37	59	32	166	18
	Arolla pine	0	.	0	.	0	.	16	50	20	45	0	.	36	33
	other conifers	8	71	4	100	0	.	0	.	0	.	0	.	12	58
	beech	91	23	12	58	21	52	4	100	0	.	16	50	144	18
	maple	6	100	4	100	4	100	0	.	0	.	0	.	13	59
	ash	14	58	0	.	4	100	0	.	0	.	0	.	18	50
	oak	43	32	4	100	10	72	0	.	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	69	31	73	30
	other broadleaves	14	59	0	.	0	.	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	417	11	285	13	178	21	163	18	192	15	265	14	1500	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	44	35	61	30	12	58	12	58	23	40	164	17
	fir	36	37	17	50	8	71	0	.	8	71	14	58	83	23
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	39	34	17	50	10	100	66	28
	Arolla pine	0	.	0	.	0	.	12	58	4	100	0	.	16	50
	other conifers	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	beech	13	58	8	71	4	100	0	.	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	0	.	4	100	4	100
	ash	4	100	0	.	0	.	0	.	0	.	0	.	4	100
	oak	19	61	0	.	0	.	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	25	47	25	47
	other broadleaves	17	80	0	.	0	.	4	100	0	.	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	105	24	69	27	73	27	67	25	41	32	84	25	439	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	12	58	17	50	27	38	8	71	60	45	32	42	155	22
	fir	12	58	8	71	4	100	0	.	4	100	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	12	58	15	61	32	39
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	8	100	4	100	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	11	58	0	.	0	.	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	37	67	0	.	0	.	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	72	38	34	40	35	33	16	50	80	35	147	24	383	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	50089	4	50508	4	30275	6	22330	7	41265	5	13673	8	208140	2
	fir	29147	4	15395	6	7818	8	2244	19	1073	18	1808	19	57483	3
	pine	4473	10	1603	28	1143	26	6190	14	6515	13	496	41	20420	7
	larch	1329	18	638	26	602	28	11658	7	7266	10	8958	8	30450	4
	Arolla pine	0	.	130	76	124	73	2369	16	3500	14	62	56	6184	10
	other conifers	2052	16	286	48	451	28	143	48	1051	28	49	68	4031	12
	beech	47937	3	15040	7	12199	7	2277	21	1996	17	13648	9	93098	3
	maple	13103	5	6130	9	4979	11	1440	17	808	27	1787	17	28248	4
	ash	11306	6	3177	12	3566	12	1270	22	901	28	3271	14	23492	4
	oak	6299	10	630	33	169	26	1276	24	171	51	2420	17	10965	8
	sweet chestnut	127	74	4	100	0	.	298	61	17	100	14562	9	15008	9
	other broadleaves	13415	6	6899	9	5251	12	7002	11	5949	14	19606	7	58122	4
	indeterminable	367	18	451	17	185	25	208	21	263	25	207	29	1681	9
	total	179643	2	100892	3	66761	4	58705	4	70774	4	80545	3	557321	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. 1358371. Birmensdorf, Swiss Federal Research Institute WSL

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